Gongcheng Kexue Yu Jishu/Advanced Engineering Science

"Green" power can only be produced by using renewable energy sources, which are plentiful and long lasting. Intermittent energy sources such as solar photovoltaic’s and wind energy may experience fluctuations in power production due to the weather and the amount of sunlight they get respectively. To dampen distribution-side variations and shield mission-critical loads from power failures. To protect sensitive loads from being of affected by the distribution side protection and faults DVR is commonly used popular tool is the dynamic voltage Restorer,(DVR). The focus of this study is on strengthening the resilience of a hybrid PV-Wind power system to voltage fluctuations at the grid level. When there is a voltage drop, the power flow is corrected using a Dynamic voltage restorer (DVR) equipped with batteries and super magnetic energy storage (SMES). At the point of common coupling (pcc) the magnitude and angle of the three-phase voltage are typically locked by the pre sag compensation utilized. The ML-DVR circuit is shielded from interference thanks to the transformer, which isolates it from the mains. When building the ML-DVR, it is essential to consider the VSI capacity and the link filter values that connect the injection transformer and the inverter. The purpose of this study is to provide a novel design for a Dynamic Voltage Restorer (ML-DVR) Smaller link filter values and higher voltage source inverter (VSI) capacities increase the system's capacity to dampen voltage harmonics, swell, and sag in response to a wide range of fault circumstances. There is hope that the innovative RLC filter can do away with switching harmonics. A lower inductance requires less of a dc voltage supply. Furthermore, the modern ML-DVR architecture has the potential to enhance voltage quality by the battery and super magnetic energy storage (SMES) based DVR isused as a compensating device incase of voltage sag condition. It has been shown how the model's RLC filter parameters are organized generally. The new DVR, as recommended by ML, is a MATLAB-designed and -simulated Dynamic Voltage Restorer (DVR) with control over the voltage.

Reliable power filters that lower current and voltage harmonics are crucial to the operation of modern power grids. In this article, we'll go through the steps you need to do to create a PV-UPQC that will enhance the quality of your electricity. Series and shunt APF are combined into a single circuit in PV-UPQC. Shunt Active Power Filters utilize the instantaneous reactive power theory (P-Q Theory) to address current harmonics, whereas series APF makes use of the dq theory to address voltage problems like voltage sag/swell. In order to power the DC-Link, a buck-boost converter is connected to the PV system through a maximum power point tracking (MPPT) algorithm. A proportionalintegral (PI) controller is used to regulate the DC-link voltage. Hysteresis current control may be used to produce the gate pulses that activate the VSI switches. In this paper, we construct a full PV-UPQC in MATLAB/Simulink and test it out with a variety of nonlinear loads. If implemented, the proposed UPQC would ensure that THD remains below acceptable limits, even in the presence of severe defects like LLLG problems. The proposed filter has been shown to be effective in simulations, reducing harmonic distortion to levels much below the IEEE limitations.

Integration of Renewable Energy into the GRID leads to reducing losses but improves power factor issues. The usage of non-linear loads leads to harmonics in the current waveform. In this paper Solar Photovoltaic with MPPT and Boost Converter is integrated to DC link of three phase Inverter with three phase BLDC Motor with an aim of reliability in Water Pumping system for both grid and Islanding Condition. Bi-directional flow of power between the single-phase grid source to solar photovoltaic system is done by using Unit Vector Template (UVT). In this work, Artificial Intelligence-based Controller (Artificial Neural Network ANNCs) were considered with the objective of reduction of THD and improving empower factor. To show the performance of proposed techniques or controllers comparison analysis is carried out with the methods available in the literature. The proposed techniques give superior performance. The proposed system is simulated by using the MATLAB R2016a version.

Plant species detection using deep learning methods has gained massive attention in the last few years due to the promising results obtained by different research communities using the deep learning approach. However, there are numerous plant species present in real life and their accurate classification based on some digital images is a quite challenging and critical task. Therefore, research on Plant species identification is of great importance. Thus, a Convolution Neural Network Architecture (CNN) based Plant Classification (CNN-PC) Model is presented in this work to efficiently classify a large number of plantspecies and correctly detect which image belongs to which species. The proposed CNN-PC model generates pre-trained weights based on the given input plant image data using different layers, blocks, and pretrained functions and packages to handle dependencies. The Vietnam Plant (VNP-200) dataset is utilized to evaluate the classification performance of the proposed CNN-PCmodel. Multi-class classification is performed to evaluateclassification results. The obtained classification accuracy considering all 200 classes is96.42%.

With expanding force of calculation and information stockpiling in this day and age, the idea of gathered information is likewise moving quickly from organized to unstructured information. The most well-known unstructured information gathered is the text information. These information can give significant experiences about the specific circumstance and help going with choices in light of the result of the bits of knowledge. Text order is one such piece of text information investigation where the named information is exposed to an AI model to recognize which text has a place with which class. Hence, this study comprehends the setting behind the text is to distinguish the associations of the words that show up regularly together. Sogrouping such words in bunches, it is feasible to have a relevant measurement and thus support text classification.This paper presents a clever methodology of consolidating message grouping and hostile to word reference word extraction as information pre-handling move toward further develop the characterization models. The dataset for this study contain is an assortment of news stories that are marked as 'phony' or 'genuine'. In this study the message information is exposed to TF-IDF vectorization which makes a meager framework andeach column in the lattice addresses a vectorized structure. In view of these vectors, agglomerative bunching is executed, and the information is appointed to two new groups as an additional component.

The goal of electrical distribution networks is to restore electricity with as little resistance as possible over the network's architecture. This article went through many revisions before being accepted on February 22, 2019. The most likely strategy for doing this is to restructure artificial ant colonies. Quickly restoring full service is essential. Once the malfunctioning part is removed, the system may function normally again. Power outages are a certainty if something occurs. To provide continuous power, these loads must be supplied by nearby healthy feeders via reconfiguration without affecting routine, load balancing, or other needs. There is a 30%-40% Holmic loss that is taken into account by the manner of distribution. A power flow study should be performed to determine the bus voltages, branch currents, and power losses in the system before any changes are made to reduce these losses (i.e., real or Holmic or copper loss and reactive power loss). The suggested research employs a direct load flow analysis to get to the bottom of problems. The IEEE 33 bus and IEEE 69 bus testing systems help minimize harmonic losses, while the IEEE four feeder testing system facilitates power restoration in the event of a blackout..

During the recent past ,the problem of early deterioration of concrete structures and durability of concrete structures has remained major issue posed to engineers . we have reported here the incorporation of Metakaolin ,fly ash ,GGBS and silica fume in binary and ternary blended system with diverse Water binder ratio. Various .concrete mixes with supplementary Cementitious material has been prepared and Steel Fiber & plasticizer dosage has been varied. Different types of tests carried out for determining the various durability properties such as such as RCPT, Chloride diffusion and corrosion initiation time An attempt has been made in this study to develop a tool for service life estimation of concrete structure. mathematical model for service life prediction presented here also suitable for existing concrete structures exposed to natural environment.

A dramatic increase in the manufacturing and usage of single-use, disposable face masks has been seen during the COVID-19 epidemic. By failing to properly dispose of worn face masks, a new type of nonbiodegradable plastic trash that will take hundreds of years to decompose endangers the environment. Therefore, there is a pressing need to recycle such garbage in a way that is ecologically benign. This study offers an effective method for producing cost-effective, green concrete that is ecologically beneficial by using waste masks that have been crushed or fibered. This study evaluated the mechanical and robustness characteristics of waste masks made using concrete. For standardized testing to assess compressive strength quick chloride penetration test, a total of six mixtures were created (RCPT),. While crushed masks were only utilized at 0.5%, the percentages of mask fibres used were 0.5, 1, 1.5, and 2% of concrete by volume. Both kinds of the mask waste were determined to be appropriate for use in concrete. It was discovered that 1% of waste mask fibres was the ideal amount to improve compressive strength and decrease chloride permeability. In addition, 0.5% crushed mask fibre also worked well, particularly when creating concrete that is more resistant and less permeable. Thus, it is confirmed that waste masks that worsen global pollution may be used responsibly to support the construction of green buildings. Circular economy, sustainability, and effective waste management are achieved by recycling discarded masks to create new concrete with greater strengths and durability.

To extracting information from chest X-ray data, Deep Learning models have demonstrated exceptional performance and numerous benefits, thanks to more powerful computer resources and improved training methodologies. This is one of the most common imaging tests, and the expanding demand for it is reflected in radiologists' increased workload. As a consequence, computer-aided diagnostic tools in the healthcare business would be advantageous since they would enable clinicians to prioritise certain tests and further identify possible ailments. No publication has particularly examined relevant work on anomaly identification and multi-label thoracic pathology categorization in the literature to the authors' knowledge. For the sake of comparison, the top chest X-ray-based deep learning algorithms have been chosen for this study. Recent advances in deep learning technology have enhanced the performance of a variety of medical image analysis tasks. Because chest radiographs are the most often performed radiological exam and have a broad variety of applications that have been studied, they are a particularly important modality. The public release of several large chest X-ray datasets has sparked academics' interest in recent years. There are extensive explanations of all publicly available datasets as well as descriptions of commercial solutions that are currently in use in the field. "Chest X-ray image classification is a strongly disputed issue in the realm of medical image analysis and computer-aided diagnostics for radiology". "The major goal of this project is to improve the quality and efficiency of radiologists' work by creating and deploying an automated technique for recognising and categorising diseases". “This study aims to improve existing surveys by concentrating on chest X-ray image classification approaches that use machine learning methods, employing chest X-ray image classification techniques based on machine learning methods". At the start, a quick introduction to data mining is given, as well as a basic comprehension of medical image processing and chest radiography.

Climatic situation presume a vital part in our day by day lives. Gathering information on diverse climate boundaries is significant for indoor and natural arranging. Behind advance in Internet of Things create information collection simpler. In this structure, several programmed and easy sensors, intended for instance, DHT11, BMP180, LDR and the ULN2803 level are utilize to measure environmental margins. This information comes as of the in turn sensor and is peruse by the Raspberry Pi hand itself and set away in CSV arrangement and content documents. The sensors collect information commencing diverse environmental margins and nourish it through a Raspberry PI so as to go regardinglike a base station. A position and adaptable function shaped utilizing Google Data Studio and Android Studio separately to illustrate current weather circumstances into a graphical show accessible to supervisor and patrons access. Clients will obtain weather caution in to unambiguous are a resting on the interpersonal group constantly and casually. Weather estimate be made into a concise time frame, permit customers to get set for their uncertain preparations within the following 30 minutes.

With the faster development and advancement in surveillance and intelligence field such as the use of Unmanned Aerial Vehicles (UAVs) shows significant importance of object detection and movement understanding. However, data is gathered from varied locations, and the classification of that abundant amount of data is a challenging process. Thus, vehicle identification in aerial images is an essential and important research area. Therefore, a Selection Decision-Classification (􀜵􀝁􀝈􀜦􀝁􀝏- 􀜥􀝈􀜽􀝏) model in the Convolutional Neural Network (CNN) is adopted in this work to efficiently classify a large amount of vehicular data and correctly identify which data belongs to which class. The proposed 􀜵􀝁􀝈􀜦􀝁􀝏􀜥􀝈􀜽􀝏 modelis transformed into the Selection and Decision Network (􀜵􀝁􀝀􀝁􀜰􀝁􀝐) and Classification Network (􀜥􀝈􀝏􀜰􀝁􀝐). Here, the 􀜵􀝁􀝀􀝁􀜰􀝁􀝐 model works on the essential image region selection and generation of preliminary weights and the 􀜥􀝈􀝏􀜰􀝁􀝐 model performs an efficient training and performs classification process. The event Recognition Aerial (ERA) dataset is utilized to evaluate the classification performance of the proposed 􀜵􀝁􀝈􀜦􀝁􀝏􀜥􀝈􀜽􀝏 model. Binary classification is performed to test the model and its efficiency. The obtained classification accuracy using Traffic-Collision is 56.7%and Traffic-Congestion is 69.8%.

In order to properly evaluate a Z-source inverter while it is powering an induction motor, closed-loop operation is required. The induction motor and Z-source inverter are in a state of closed-loop control. To improve inverter performance and reduce harmonics, we have opted to use multilevel inverters rather than the more common single-layer kind. In order to get the most out of an induction motor, you need to use a variable-speed drive to control its speed. Z-source inverters precisely regulate the input voltage drop by using the peak dc link voltage, while also filtering out transient disturbances such the input voltage ripple and the load current. In order to adjust the inverter's boost and the induction motor's output frequency, the switching method employs pulse width modulation (PWM) control. In this article, we look at how an induction motor's speed is controlled by a PI controller and how it compares to that of a fuzzy logic controller. When applied to a 1.8 kW induction motor driven by a Z source multilevel inverter, the necessary speed control yields positive results from PSIM simulation, indicating proper performance analysis. In both dynamic and steady-state simulations, the proposed technology is proven to be superior to the conventional voltage source inverter fed induction motor.

The development of classical art forms in the area of modern architecture is an important way to represent the nation and the process of devotion to nature. The form of classical art and culture is the traditional way to connect different religions, cultures, faith, diversity, lifestyle, architecture, paintings, and various sculptures and to perform as a gateway of spiritualism. The survey of 2022 notifies various cultural and traditional tones of Indian classical forum is an influence of different religions and processes to promote nation in the area of the universal platform. As per the international report, India is one of the largest collections of traditions, art, culture, belief, and rituals. Indian art and culture are known as Intangible culture Heritage of humanity or ICH. The report of 2021 identifies India as progressing through the journey of cultural activities, beliefs, religion, and its cultural influences. The whole study focuses on the areas of important aspects of Art and culture, a system of influence on modern architecture. The present study also covers the area of critical analysis of classical art.

Co type and MnZ type at atomic and magnetic disorder are investigated for Co based Heusler alloy Co2MnZ (Z=Si, Ge, SN) as a function of quenching temperature. It is shown that Co type disorder preferentially proceeds with increase in quenching temperature while the MnGe type disorder preferentially proceeds in Co2MnGe. In Co2MnSn only the Mn-Sn type disorder proceeds. On the other hand decrease with in quenching temperature Co2MnGe, while it remains almost constant Co2MnSn.

It completely decomposes to a mixture of a tetragonal phase and a Cu9Al4 phase. Electronic structure, magnetism and face stability of new Mn-based Heusler alloys Mn2CuAl has been studied and synthesized by first-principles calculations and by the melt-spinning method. Firstly, the calculations suggest that Mn2CuAl crystallizes in the Hg2CuTi type of structure, in which the Cu atoms have Al as nearest neighbors. As a consequence, the Mn atoms occupy two nearest neighbors’ sub lattices A and B. Like the well knownHeusler alloyCu2MnAl, the magnetic moment of ,Mn2CuAl also comes from the 2Mn atoms in the lattice, while the Cu atom is almost nonmagnetic .At equilibrium lattice constant, Mn2CuAl is a ferrimagnetic with moment of 0.22uB. The partial spin moments of Mn (A) and Mn (B) are -3.52uB and 3.74uB, respectively. The small total moment comes from the antiparallel configurations of the Mn partial moments. With a small contraction of the lattice, the total moment becomes near zero and half-metallic antiferromagnetic state is observed. Secondly, It has an ordered bcc structure and is a ferrimagnet with a saturation moment of 1.44mB/f.u. at 5 K. The magnetization mainly comes from the contributions of the antiparallel aligned Mn spin moments. A compensation point is observed at 630 K, indicating the antiferromagnetic between the two Mn sublattices. The Curie temperature of the ribbons is 690 K When heated to 740 K, the Mn2CuAl.

Co type and MnZ type at atomic and magnetic disorder are investigated for Co based Heusler alloy Co2MnZ (Z=Si, Ge, SN) as a function of quenching temperature. It is shown that Co type disorder preferentially proceeds with increase in quenching temperature while the MnGe type disorder preferentially proceeds in Co2MnGe. In Co2MnSn only the Mn-Sn type disorder proceeds. On the other hand decrease with in quenching temperature Co2MnGe, while it remains almost constant Co2MnSn.

The electronic and magnetic properties of heusler alloys compounds Fe2MnAl, Fe2MnSi and Fe2MnGa are described in this article. We have applied the full-potential linearized augmented plane waves plus local orbitals (FP-L/APW) method based on the density functional theory (DFT). Generalized-gradient approximation (GGA) is used for the exchange and correlation potential. The calculated atomic resolved densities of states of Fe2MnAl, Fe2MnSi indicate half-metallic behavior with vanishing electronic density of states for minority spin at the Fermi level, which yields perfect spin polarization while for Fe2MnGa the full electron energy of ferromagnetic metallic fcc phase (μfcc =6.11μBf.u.) is lower by about 0.06 meV (0.69 K) than that of ferrimagnetic half-metallic phase with L21 structure (μL21 = 2.036μBf.u.).

The electronic, magnetic, and structural properties were investigated for ZrRhTiZ (Z = Al, Ga) quaternary Heusler compounds by using first-principles calculations framed fundamentally within density functional theory (DFT). The electronic structures obtained revealed that both compounds have half-metallic characteristics by showing 100% spin polarization near the Fermi level. The half-metallicity is robust to the tetragonal distortion and uniform strain of the lattice. The total magnetic moment is 2 µB per formula unit and obeys the Slater-Pauling rule, Mt = Zt − 18 (Mt and Zt represent for the total magnetic moment and the number of total valence electrons in per unit cell, respectively). The ferromagnetic ground state and thermodynamical ground stability of the compound are are supported by relative total energies. The investigated Curie temperatures of the compounds exceed room temperature indicating that these compounds are promising candidates for beyond room temperature spintronics and magneto-electronics applications.

Nowadays multilevel inverter (MLI) technologies become extremely main choice in the area of high power medium voltage energy control. Although multilevel inverter has a number of advantages it has drawbacks in the layer of higher levels because of using large number of semiconductor switches. This may leads to large size and price of the inverter is very high and also increase in losses. So in order to reduce these difficulties in the new multilevel inverter is proposed to reducing the switches. The increase in the level of output, number of switching equipments besides with the switching states enhances. As a consequence, higher switching losses occurs that prompts power loss. Accordingly, the efficiency of the complete conversion network diminishes. The significant characteristics of this submitted work is that the module can be accomplished as sub multiple level assembly. Progressively, with minimal rise in the switching elements, all number of levels can be elongated. This paper is subdivided into introduction explaining in short about the structural components of the inverter then after that analysis of the proposed inverter that is switching strategy and operational principles are given. This paper represents Symmetric Cascaded Multilevel inverter which utilizes variable frequency carrier predicated pulse width modulation techniques. This topology helps in decreasing total harmonic distortion and helps to reduce the switching losses for various level inverters. The simulation study about the performance and operation of the suggested topology has been performed in MATLAB/SIMULINK. The objective of this paper is to implement less number of switches and DC power source to achieve a different level of MLI as compared to the traditional converter topologies.

The sun and wind primarily based time are well thoroughly taken into consideration to be alternative source of environment-pleasant electricity period that could alleviate the power demand concerns. This paper provides a standalone hybrid power technology device together with sun and permanent magnet synchronous generator (PMSG) wind electricity resources as well as an a/c loads. A managerial manage tool, made to put into effect Maximum power point tracking (MPPT), is delivered to make nice use of the simultaneous electricity harvesting from fashionable electricity generation beneath various climate situations. This paper offers an evaluation of demanding situations and opportunities/ options of hybrid solar PV and also wind power assimilation systems. Voltage in addition to frequency exchange, in addition to harmonics is predominant electricity high-quality concerns for each grid-related as well as standalone structure with bigger effect in case of weak grid. This can be dealt with to a massive degree with the aid of having appropriate layout, superior short feedback manipulate facilities, and terrific optimization of the hybrid systems. These renewable useful resource assets are top rate options to satisfy the world electricity ask for, nevertheless unforeseen as a result of natural troubles. Using the crossbreed solar at the side of wind green beneficial useful resource tool along with can be one of the handiest selections for the use the ones without difficulty provided assets. The objective of this researches paper is to test out the sum of elements of Hybrid sun in addition to wind machine. The application in addition to particular standards connected to the increase of crossbreed similarly talked about on this paper. In this paper, simulation version of crossbreed sun at the facet of wind power device connected to grid is carried out. For this evaluation is carried out on alternative layout to pick out Harmonics, interruptions in resource voltage, supply contemporary, in addition to similarly section of THD.

When it comes to resolving the vast range of problems that crop up in regular life, the cyber physical system (CPS) is by far the most popular infrastructure option. However, making the right decision quickly remains challenging in the big data era. Transforming the manufacturing sector and other applications will rely on Internet of Things (IoT) or CPS. These advantages of CPS come at a price, however, as companies struggle to deal with the massive amounts of data being produced by Internet-connected gadgets, resulting in a slew of difficulties for individuals. These infrastructures are too complex for even the most knowledgeable individuals to manage, monitor, or evaluate. As a result, there is a pressing need for the convergence of Machine Learning (ML) and cyber security in CPS, which equips experts with the tools they need to monitor the internet for potential threats in record time. Since the proposed study examines the various frameworks used for Cyber-attack detection using a learning method, it demonstrates the significance of ML and Deep Learning (DL) in a CPS for more accurately identifying potential dangers. Many academics rely on security analytics, and the tool may also be used to prioritize alarms and signals. It has been brought to the attention of the researchers that the suggested study on various assaults has also emphasized the need to be mindful of rare attacks that may become highly harmful. Additionally, the pros and disadvantages of various methodologies and datasets used in the study of various works in assessing different assaults are presented to aid in selecting the appropriate strategy according to demand.

As organizations throughout the globe sought creative software experiences in respond to economical, legal, and technical constraints, digitalization became a fundamental concern. We offer a conceptual model in this document that simplifies the govt internet strategy, simplify construction design, ethnic and economic transition with capacity building, monitoring and data oversight, and information delivery methods in a trustable and developed a sense, resulting to innovative and beneficial use of big information data analysis in lawmaking and the usage of internet technologies. We further examine particular adaptations of the structure in the situation of the Indian ministry, including the application's user acceptance, giving tangible proof of the platform's practicability.

The exploration and commercialization of petroleum and energy is a data-driven economy. The generation of massive volumes of data has laid the ground- work for the application of Big Data Analytics (BDA), mining data resources, and using this information to drive oilfield production techniques are all part of the core technologies of the oil and gas industry. Huabei Oilfield has been experimenting with the use of big data analytics in oil and gas production in recent years. Taking into account the various categories and attributes of oilfield information, a "seven-step method" for closed-loop BDA has been developed, which includes data collection, processing, tracking, and evaluation. A Hadoop/Spark-based data-mining platform for oil production engineering has also been developed

Using ad-hoc networks in automobiles, timely data transfer may reduce road accidents. Due to limited transmission capacity, the vehicular ad-hoc network distributes data over many hops. The network's dynamic design generates frequent route disconnection due to mobile vehicles. Despite to these constraints, timely message delivery continues to be an issue. This study discusses the problem of timely message transmission in vehicular communication networks. This research presents a secure and efficient data routing strategy for heterogeneous ad hoc networks. First, node trust is calculated using an entity-centric paradigm. This also assists in determining the network's connection durability and collision likelihood. For timely distribution, an improvement in connection reliability and duration is necessary. Additionally, to decrease data collisions and improve network delivery ratio. Several network parameters were used to assess the enhanced model for data distribution. Experiments demonstrate a decrease in latency and packet loss ratio. In our protocol, the cloud evaluate each person's trustworthiness based on vehicle-uploaded attribute values. Using the cloud's reliability, network vehicles select trustworthy forward nodes and complete the route.

To achieve high yield and proper identification of damaged chips after production, a well-structured testing approach must be used. One way for detecting transition delay issues on system on chip is Atspeed scan testing. Atspeed patterns are applied to detect transition delay faults ,even though they stay for shorter duration .They are the cause for failures in the IC’s. In this paper, Atspeed patterns are applied for real time design under test to detect transition delay faults and able to achieve 71.85% fault coverage for stuck at type fault and 55% fault coverage for transition type fault through structural testing using 28nm technology using tessent tool. Different Fault classes like DS, DI AU are also found out for total number of faults. Fault Simulation is done for the intended design to find the faults from fault list for the deterministic test patterns using Launch on capture method.

When a computer system can perform a task without being explicitly programmed, it is known as machine learning. Learning algorithms are utilized in a variety of commonplace applications. A search engine, such as Google, is efficient because it employs a ranking algorithm that is capable of learning. These algorithms are used in image processing, data analysis and predictive analytics, to name a few. When a machine learns how to deal with data, the main benefit is that it can do better work. It retains this information so it can perform its functions automatically. This article provides an overview of the application of machine learning techniques. In addition, it provides an overview of a search algorithms utilized to solve learning issues.

MIMO-OFDM IDMA is used to reduce fading in underwater wireless communications. MIMO allows data to be delivered simultaneously from several antennas and received by one or more antennas. This strategy increased data rate, preserved bandwidth, and reduced fading. IDMA outperforms all other underwater multiple access techniques. Both MIMO-OFDM and MIMO-IDMA demonstrated low BER with variable user numbers. We combine MIMO-OFDM and IDMA to reduce burst errors and to fade in underwater channels. Underwater wireless communication fades in several ways. Hydrophones and MIMO-OFDM with IDMA improve underwater communication. Underwater communication uses acoustic waves, which have slower data rates than electromagnetic waves. This work reduces low-data-rate fading. This study used IDMA with random interleaving. Future IDMA upgrades may include a tree-based interleaved. Tree-based interleaved is used for pattern generation and user separation.

XNOR-based Modified 2-by-2 VCS scheme for secret sharing of images to a group of participants has been implemented with comparison of the results with earlier schemes. C Programming language based implementation requires to be done as an appreciation of the results as compared to earlier schemes. Same level of security as for other similar schemes for colored images has been developed for grayscale images. A 2 sub-pixel layout has been used for the implementation. Implementation of the concept has been done with less pixel expansion and distortion which is there in 4 sub-pixel layout based 2-by-2 VCS scheme.

XNOR-based Modified 3-by-4 VCS scheme for secret sharing of images to a group of participants has been implemented with comparison of the results with earlier schemes. C Programming language based implementation requires to be done as an appreciation of the results as compared to earlier schemes. Same level of security as for other similar schemes for colored images has been developed for grayscale images. A 4 sub-pixel layout has been used for the implementation. Implementation of the concept has been done with less pixel expansion and distortion which is there in 2 sub-pixel layout based modified 2-by-2 VCS scheme.

Network security is being increasingly breached with more ethereal intrusion methods, broadening the challenge of providing integrity and security to networks. During the past few years, substantial research has been conducted for fabricating new methods to thwart various network attacks. This review article scrutinizes those research contributions with the help of a lucid systematic literature review(SLR) process. The sources used for data retrieval are Web of Science, Science Direct, ACM digital library and the IEEE Xplore (Institute of Electrical and Electronics Engineers). A total of 64 crucial studies publicized from the year 2017 to thus far were carefully chosen. The history of the network intrusion evolution is specified for a better understanding of the need for intrusion detection. A comparative study of datasets used in various research studies is presented in order to evaluate the suitability of the dataset. The SLR is applied with the goal of discovering the contemporary trends in the detection of network intrusion. This review offers a comprehensive resource background for researchers interested in NIDSs. This review also discusses various challenges that need attention and has recommendations for probable upcoming research tendencies.

Problem of formation damage in oil and gas industry is very much significant and more efforts are needed to mitigate it. Reservoir engineers should have idea about the potential and nature of the formation damage. With the help of mathematical model for the cake build-up engineers can reduce this problem. There are many mathematical models for cake build up in wells. With these models MATLAB program is created. This program takes 8 parameters from user and provide data such as in cake thickness, filtrate volume escaped through the well. Graph are plotted by changing one parameter while keeping others constant. The graphs obtained help to understand relationships between different parameters such as viscosity of filtrate, pressure drop and volume fraction of solid in mud. Results show that increasing viscosity, volume fraction of solid in mud decreases formation damage. The data for three different wells were considered in different parts of the country. Whereas increasing pressure drop increases the formation damage. By controlling these parameters we can reduce the formation damage.

The need of clean and green environment, existing transport system changes into Electric vehi cles. For those EV’s we need fast and efficient charger. This work represents an improved power quality CUK converter fed Phase Shift Full Bridge converter and LLC converter for an on-board battery charger used in electric vehicle application. This topology consists of two converters, one for Power factor correction and another one for electric vehicle battery charging by using CC and CV algorithm. In this topology, the CUK converter is used for Power factor correction and Phase Shift Full Bridge converter and LLC converter is used for conversion of dc-link voltage to the dc voltage that is required for the battery charging. This Proposed Converter was designed and simulated in MAT- LAB/SIMULINK to transform 300 V input voltage to an output voltage range of 48-55V at 580W and comparing the values taking all parameters into account step by step.

On a daily basis, there are numerous actions done by human beings. The visually challenged face difficulty identifies objects without any help to get their desired objects. This is an emerging technology that assists blind people, in locating objects. In the prototype of the smart glove, the camera is connected to the A Raspberry pi with the help of a cable which will record real time things. The camera, starts capturing an object while moving the hand, of a user. The captured, object by using Deep neural network (DNN), detects an object and then object tracking gets started. By that, the desired object can be known, and voice command is given by the speakers. Additionally, includes an ultrasonic sensor where it senses any obstacle, and then produces a beeping sound buzzer and a micro-vibrating motor that vibrates that makes the user alert. This entire system can be achieved by interfacing camera, ultrasonic sensor, vibrators, and speakers to the raspberry pi 3 b and can be implemented with the help of machine learning. Where the inputs can be taken from camera and ultrasonic sensor and the results are produced from speakers, vibrating motor, and buzzer.

Approximate computing can decrease the design complexity with an increase in performance of area, delay and power efficiency for error resilient applications. This brief deals with a new design approach for approximation of multipliers. Approximate computing can decrease the design complexity with an increase in performance of area, delay and power efficiency for error resilient applications. This project proposes an accuracycontrollable multiplier whose final product is generated by a carry-maskable adder. The proposed scheme can dynamically select the length of the carry propagation to satisfy the accuracy requirements flexibly. The partial product tree of the multiplier is approximated by the proposed tree compressor. An multiplier design is implemented by employing the carry maskable adder and the compressor. Compared with a conventional multiplier, the proposed multiplier reduced power consumption. The implementation, synthesis and simulation is executed and noted in the Xilinx-ise in verilog hdl language.

As the technology increasing, implementation of automobiles in this world has rapidly Increased ,as technology increases the required safety measures also necessary to be followed if it is about motorcycles,but many drivers do not use it and get accidents and cause critical injuries sometimes they even lead to death, so the motorcyclists they even lead to death,so the motorcyclist who are not wear helmet,so helmet detection and license plate recognition using CNN helps,However due to poor video quality license plate recognition become difficult task,but using convolution neural network (CNN) made it more suitable model to obtain fast operation .since image processing is involved it detects the motorcyclists wearing helmet or not,if the motorbike is detected as no helmet ,then the license plate of the motor cycle is detected using tesseract OCR ,this model is experimented as accurate modal and can capture more number of images with clarity to detect Helmet V/S no Helmet Criteria.

A four wheeled robot specifically designed to detect the fire and extinguish the fire up on detection. This robot can manoeuvre automatically or can be controlled remotely. We use Ultrasonic sensor to make our robot detect obstacles which helps it to work automatically. The fire sensor in the robot will sense the fire and starts extinguishing if there are any fire accidents. In case of fire accidents, anyone can control the robot remotely by interfacing it to the mobile via blue-tooth module which helps us to control the robot remotely and perform fire extinguishing remotely. The mode of operation of the Real time automatic fire detecting and extinguishing robot is ‘Automatic surveillance mode and remote-control mode.’ The Real time automatic fire detecting, and extinguishing robot has many applications. It can be used in commercial complex, hotels, schools, residential areas, hospitals, and public places. Keywords: Fire detection, fire extinguishing, robot, automatic, remote controlled.

Rating Prediction is a task of aiming to forecast a user’s rating for those items which are not rated by them. Existing approaches of collaborative filtering method can neither handle large datasets or deal with user’s who have not given rating to a particular item which leads to data sparsity problem .To subdue the problem of data sparsity, In this paper , they have carried out a hybrid method which is a combination of recommendation algorithm named Stacked Sparse Auto encoder(SSAREC) and Matrix Factorization method for Rating prediction.

Nowadays, the traffic control system in our country is non-flexible due to the increase in the number of vehicles day by day causing traffic congestion. In present strategies, human control or clocks are utilized. The two important resources of the present-day system are time and fuel which are wasted in the case of traffic congestion. To overcome such problems, advanced technology to improve the state of traffic congestion has been introduced. A system that measures the vehicle density using canny edge detection to control the traffic with digital image processing is proposed. This system proposes a dynamic system that offers improvement in responsive time and efficiency. This system pre-processes the image using image processing which is captured from the camera that is installed at intersections, computes the density of the traffic and sets the timer of the signal according to the density of the traffic. This overall system works efficiently and has a quick turnaround time, saving major key resources at every intersection.

Face recognition is among the most productive image processing applications and has a major role in the technical field. Smart Attendance using Real time Face Recognition is a real world solution which comes with day to day activities of handling student attendance system. Aim of the project is to enhance the immediate attendance system of educational institution into well-organized way by building face detection software. The development of this system is aimed to manage digitization of the traditional system of taking attendance by calling names and maintaining pen and paper records. Present form of taking attendance is unexciting and time overwhelms .Attendance records can be easily influenced by manual recording. Our Proposed system verified to be an adequate and robust device for taking attendance in a classroom without any time exhaustion and manual work. The system developed is economical and need less install

To reduce the storage space and bandwidth consumed Data deduplication is one of the most important methods of data compression to eliminate multiple copies of the same data. This paper aims to principally resolve the issue of allowed data deduplication, in order to safeguard the security of data. Before outsourcing data must be encrypted with the convergent encryption technique in order to protect the security of sensitive information and to facilitate deduplication. Alongside the data the user's access level is analyzed to determine if they are an authorized user. In accordance with the definitions set forth in the proposed security model Security analysis shows that our approach is safe. We prove that, compared to traditional operations, our suggested duplicate check method is very low-cost. We have put our proposed legal duplicate-check system in use as a model and conduct tests on it. By utilizing various methods, this paper attempts to minimize the amount of duplicate data that occurs when using the cloud.

These days almost in every metropolitan city the pollution is at high rate and this all lead to global warming. To decrease the global warming, pollution we need an effective environmental planning. Environmental planning can be done if we know the environment parameters. To know the environment parameters, we do environment monitoring. In this paper we proposed a mobile controlled robotic system which is designed and implemented to monitor the environment parameters such as humidity, temperature, air quality index. This system is divided into 3 parts. ESP32 microcontroller, environment monitoring system which consists of sensors, navigation and control system which consists of motor driver, GPS module. This robot is Wi-Fi and Bluetooth enabled and it can store data on ThingSpeak IOT platform. The robot can be controlled using Android Bluetooth controller app, wherever the robot moves the data is collected at a particular place and is stored in IOT and cloud server. This data can be accessed anytime and is used to do effective environmental planning. This robotic system is cost-effective and is designed to monitor environmental parameters with minimum human intervention to avoid health risk efficiently.

Coronavirus disease (COVID-19) is an infectious disease caused by the Severe Acute Respiratory Syndrome corona virus 2 (SARS-CoV-2) viruses. With a motive to detect and diagnose onset of COVID-19 diseases caused due to SARS-CoV-2 chest radiographs (X-rays) combined with deep convolutional networks (CNN) methods are being used. One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. But, there will be issues regarding accuracy, imbalanced datasets and their performance. To deliberate these issues various networks such as Dense Net, Resnet 101, Inception Net, Resnet 50, VGG16, and VGG 19 have proposed. Results are obtained in terms of precision, FSCORE, Accuracy and Recall using the datasets .Methods such as VGG16 and dense net provide 99.8% accuracy on the dataset, which means that these methods more accurately identify COVID-19 patients. A pilot test of VGG16 models on a multi-class dataset is being presented, showing promising results by achieving 91% accuracy in detecting COVID-19 and normal patients. In addition to that, the paper establishes the models (Resnet 101, Resnet 50, and Inception net) having poor performance having accuracy up to 78%. Still, model like VGG19 demonstrates an accuracy of 93% on both datasets, which postulates the effectiveness of our proposed methods, ultimately presenting an equitable and accessible alternative to identify patients with COVID-19.

This paper introduces a digit-level serial-in parallelout multiplier using redundant representation for a class of finite fields which uses a Galio field multiplication (GF 2^8) it uses the characteristic two finite field with 256 elements which can also be called as Rijndael’s it uses the reducing polynomial for multiplication x^8+x^4+x^3+x+1.Here we are using not only the GF method we are also using the cyclotomic field which means the first and last binary bits of 4bit have the same binary bit. Mainly we are using here finite field multiplication to reduce the redundancy.

Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features,however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labelled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT).Second, we describe how SMT trains a stacked denoising autoencoder to encode and reconstruct the call graph forend- to-end deep learning,Third, we analyze the results of empirically testingSMT on both synthetic datasets and production applications with intentional vulnerabilities. Datasets and feature vectors are crucial for cyber-attack detection systems. The following feature attributes were chosen as the input for our supervised learning algorithms..In this paper evaluating proposed AutoEncoder Algorithm with SVM , Naïve Bayes and LSTM.In extension work we are using Quantum SVM algorithm and comparing with all algorithms.

The study of fuel utilization prediction explores the accuracy of machine learning algorithms in forecasting fuel utilization for bulky vehicles. The most important aspects for predicting fuel consumption are related to road grade, vehicle speed, traffic, weather condition, and so on. Machine learning methods are most successful, in forecasting fuel utilization and identifying which aspects are most dominant for fuel consumption. The main motive of this project is to increase the accuracy of the fuel utilization forecasting model with machine learning to reduce fuel utilization. By reducing the utilization of fuel there are many benefits in satisfying domain needs and business economic improvements. The new model encapsulates procedure based on distance traveled rather than the conventional methods where each individualized machine learning model is developed for fuel usage. The new model can easily be evolved for an independent vehicle in an agile in order to evaluate fuel utilization over the entire agile. This procedure is used in concurrence with seven forecasts obtained from machine speed and road grade to obtain a neural network model for fuel utilization in heavy vehicles. Forecasting of fuel utilization using a machine learning model algorithm ANN would provide better accuracy when compared to other algorithms. The forecasts of the model are accumulated over stable window sizes of mileage traveled. Dissimilar window dimensions are estimated and the outcome shows that a 1km window can forecast fuel utilization with a 0.95 quantity of persistence.

Cleanliness plays a crucial role in developing smart cities and cleaning garbage in urban areas has become a challenge to local governing bodies. To tackle the situation, we propose an urban street cleanliness assessment approach in three phases using advanced technology i.e., Mobile Edge servers, R-CNN Deep learning & assessment of the approach. Initially, high resolution cameras will be installed on the vehicles to collect the street images. Mobile edge servers are used to store and extract street image information temporarily. Secondly, the primary data collected from Mobile Edge servers were transmitted to the cloud data center for thorough analysis via available city networks. Also, Faster Region-based Convolutional Neural Network is used to identify the strategically located street garbage locations & categories. The results will help the city managers to arrange necessary clean-up personnel effectively & efficiently.

Deep learning models for food recognition that are now available do not allow for data incremental learning and frequently suffer from catastrophic interference difficulties during class incremental learning. Because real-world food databases are open-ended and dynamic, with a constant rise in food samples and food classes, this is a critical challenge in food recognition. To deal with the dynamic nature of the data, model retraining is frequently used, although it necessitates high-end computer resources and a significant amount of time. By combining transfer learning on deep models for feature extraction, Relief F for feature selection, and an unique adaptive reduced class incremental kernel extreme learning machine (ARCIKELM) for classification, this study offers a new open-ended continual learning framework. Transfer learning is advantageous because deep learning has a strong generalisation capacity.

The project is based on implementation of modal radar target direction and distance identifier using iot. The objective of our project is signal detection for both stationary as well as moving target. It is an acronym for radio detection and ranging to determine range, altitude, direction and speed of both moving and fixed object. In this project the radar is fitted with DC motor and its operation is controlled by microcontroller which is interfered with radar target identifier system has an array of IR pair for stationary object and ultrasonic pair for moving target. These sensors keeping track with target in all direction and If the target is found to be moved in any direction and then it transmit control signal to microcontroller which will communicate with IoT module which are being used for wireless communication between transmitter and receiver. The status of an target is displayed on LCD for user identification and buzzer will indicate target detection for alert. The outcome result will be précised, accurate and cost effective with all parameters of target.

The main aim of this project is to implement solar tracker system and failure detection. The sun is tracked by using Light Dependent Resistor’s and its position changed in such a way that it generates efficient power output as compared to fixed panel. The solar panel is moved with the help of servomotor, so that sun’s light is able to remain aligned with the solar panel .Power failure detection is recognized by with the help of IOT platform i.e., Blynk App. Blynk app is used to identifying the voltages of solar panel and sensors respectively. This project is low at cost andproductive.ESP32micro controller has bluetooth and wifi connectivity option to communicates with mobile to the IoT platform, where data restored, processed and can be accessed using a computer or any smart device from anywhere. The system updates data from sensors to IoT server for every10 seconds. The stored data can be used for further analysis climate, to reduce pollution, save energy and provide an overall living environment enhancement for smart city applications.

LPG, widely used for cooking, for comfort or due to the fact it's far the desired gas source. This examine specializes in the usage of the Internet of Things to degree and display the gas level of a residential LPG cylinder, which leads to the auto booking of new LPG cylinders and the gas leakage detection. In our project LPG level is shown on LCD. Load sensor (SEN-10245) is used to measure gas level. Gas leaks are detected using gas sensor (MQ-6). From the date of initialization, we can determine the validity of LPG usage. Using IOT the user is alerted when they get notification to their mobile. when the LPG level is less than threshold i.e., 50, Automatic booking of new LPG is done through a mail. With the help of gas sensor leakage is detected.

Using RFID and fingerprint modules, Vehicle and License Authentication detects the details of a person as well as their licence. with the assistance of GSM. If a person forgets to bring their licence or their documents have expired, they will receive a message informing them of the fine amount they must pay. The fingerprint sensor is utilised to detect a person's licence, making it very easy for traffic police to locate the person regardless of whether he or she has a licence or not. The traffic may be efficiently regulated using this idea. There is no requirement for the individual to have a valid driver's licence. It reduces the pressure on traffic police and the time it takes to verify licences.

In our project we have advanced a platform in which we are controlling our robot distantly through internet. It allows us to monitor the places in the remote and susceptible areas. Our robot system will keep track of the site and can go into those areas where human access isn’t feasible. The camera ascended on the robot will continually capture the video. The live stream from the robot camera will be visualized on web page and will be used for both surveillance and controlling the robot movement correspondingly. The movement of the robot is executed using CGI scripting and examination is done using the MJPG video streamer. Our aim is to control the robot and keep track through web page .

Now-a-days transportation has become great difficulty to the individual to reach the destination on time, every one are having their own vehicle. And the people with all body parts are fortunate but when it comes to physically challenged people it’s very unfortunate that the people with partially disabled with hands can’t drive vehicle with the help of steering. In the Buses or Trains they are provided with minimum reservation and which will be very disappointing and they also don’t dare to buy a vehicle and assist with a driver which will cost a lot .So this project will be a great solution for them. The person who is driving car will be equipped with a device which is placed around the neck of the person who is driving the car which is helpful to move the steering forward and reverse direction without any physical or mental stress .The project uses 2 geared motors of 60RPM to drive the prototype of car. Also this car can take sharp turnings towards left and right directions. This project uses Arduino MCU as its controller. We are also using four switches in the circuit which will be ON when the person will move neck forward and backward.This project uses 12V Lead Acid battery which drives two DC motors with the help of H-Bridge Circuit

These days, most people who plan a trip are first taking initiation to search the locations through the internet. However, travelers usually have a very limited knowledge about the locations and the information about the locations. The tourists can actually find a lot of information about the places through surrounding people and internet. But, it takes a very large time for him to search and the places in an organized way. So, this paper proposes a Recommendation System for tourists based on their interests. The interests of tourists are known by accessing their social media profile, based on their ratings that are given by them during the previous trips and based on the reviews.

Nowadays, the number of accidents is so high and uncertain. Accidents causes worst damage, serious injury and even death. These accidents are mostly caused by delay of the driver to hit the brake. Preventive measure such as improving visibility, auto headlights, windshield wipers, tire traction, etc. were deployed to reduce the probability of getting into an accident. Now we are at the stage of actively avoiding accidents as well as providing maximum protection to the vehicle occupants and even pedestrians. Hence in this paper, we make an attempt to propose a new automated vehicle collision avoidance system. This project is designed to develop a new system that can solve this problem where drivers may not brake manually but the vehicles can stop automatically due to obstacles by using sensors. Thus, this paper focuses on the development of a sensor based embedded system that can assist the drivers to avoid any sort of collision on the road in order to save the precious lives and also to prevent the financial loss.

Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person’s mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.

Android was the most popular mobile operating system amongst smart phone users. Its high popularity, combined with the extended use of smart phones for everyday tasks as well as storing or accessing sensitive and personal data, has made Android applications the target of numerous malware attacks over the last few years and in the present. In this paper based on the relevant features from the set of permission by combining genetic algorithm and simulated annealing, and three algorithms GASA-SVM, GASADT, and GASA-KNN are developed based on this approach. The Drebin dataset with feature selecting actives are used to compare the malware accuracy. The system improves Android malware detection accuracy, and the GASA-SVM with the best value of 0.9707 has the best result.

Intentionally deceptive content presented under the guise of legitimate journalism is a worldwide information accuracy and integrity problem that affects opinion forming, decision making, and voting patterns. Most so-called ‘fake news’ is initially distributed over social media conduits like Facebook and Twitter and later finds its way onto mainstream media platforms such as traditional television and radio news. The fake news stories that are initially seeded over social media platforms share key linguistic characteristics such as making excessive use of unsubstantiated hyperbole and non-attributed quoted content. In this paper, the results of a fake news identification study that documents the performance of a fake news classifier are presented. The Textblob, Natural Language, and SciPy Toolkits were used to develop a novel fake news detector that uses quoted attribution in a Bayesian machine learning system as a key feature to estimate the likelihood that a news article is fake. The resultant process precision is 63.333% effective at assessing the likelihood that an article with quotes is fake. This process is called influence mining and this novel technique is presented as a method that can be used to enable fake news and even propaganda detection. In this paper, the research process, technical analysis, technical linguistics work, and classifier performance and results are presented. The paper concludes with a discussion of how the current system will evolve into an influence mining system.

Technology evolution has bought many changes in the field of science and all other fields.One among that field is robotics.Robot is a machine developed by humans in order to increase the work efficiency and reduce the man power. Fire extinguishing is the one of the tasks used for. .Firefighting is the major task because the results occurring due to these accidents are major and life threatening. So, Fire Fighter Robot is developed in order to extinguish the fire from a safe distance and safe guard human lives. The Fire Fighter Robot developed has night vision camera. Robot is designed in such a way that it moves in all the four directions and robot is controlled by raspberry pi.Fire fighter robot has sensors for fire detection, cameras to record the and check the surroundings and motors to give the driving force to the robot. Recordings of camera can be seen through the computer interface.

Code Division Multiple It is basically a channel access method and is also an example of multiple access. Multiple access basically means that information by several transmitters can be sent simultaneously onto a single communication channel. Hence, with the help of CDMA, multiple users can share a band of frequencies without any kind of undue interference. Trouble CDMA interconnect (OCI) to update the utmost of CDMA sort out on-chip (NoC) crossbars by growing the amount of usable spreading codes. Serial and parallel OCI outline varieties are acquainted with hold quick to different region, deferment, and power essentials. A 65-center point OCI-based star NoC is completed, surveyed, and differentiated and a corresponding space division various passage-based torus NoC for various made development plans. The result with respect to the benefit utilization and throughput include the OCI as a promising development to complete the physical layer of NoC switches.

In Cyber Security, One of the major challenge is the provision of automated and effective Cyber-threats detection technique. In this paper we use Artificial intelligence technique for cyber threat detection based on artificial neural networks. In this technique it collects security events to individual event profiles and uses a deep learning based detection method for cyber threat detection. The different artificial neural network methods are FCNN, CNN, LSTM. It uses data sets from real world and compares the performance of different algorithms. The different machine learning methods are SVM, K-NN, RF, NB, and DT. At the end it compares all the different algorithms and chooses the best algorithm for cyber threat detection.

This research investigates a hybrid model for stock Market prediction that combines a K Nearest Neighbors (KNN) approach with a Probabilistic strategy. The assumptions suggested by distance function are one of the fundamental challenges with KNN classification. The assumptions are based on the test instances closest Neighbors, which are at the centroid of the data points. This method eliminates non centric data points from equation, which can be statically important in predicting stock price movements. To do this , an upgraded model must be built that combines KNN with a probabilistic technique that computes probability for target instances using both centric and non-centric data points . Baye’s theorem is used to create the integrated probabilistic technique. KNN , Naïve Bayes , one Rule (oneR) , Zero Rule were used to evaluate the proposed hybrid KNN Probabilistic model against conventional Classifiers ( ZeroR) .Keywords – Stock Price Prediction, K-Nearest Neighbors, Bayes’ Theorem, Naïve Bayes, Probabilistic Method.

This paper proposes a new control technique based on a Meta Heuristics Gravitational Search Algorithm (GSA) method for the BLDC motor to control its speed. The Gravitational Search method is used to dampen the PID loop that controls the BLDC motor's speed. This is an optimization technique based on gravity and mass. This modern technique is successful in optimizing the parameters of the integral square error of the PID controller [18]. Initially, the speed was controlled using a conventional PID controller but some amount of speed error remained. As a result, the proposed technique has been benchmarked for properly controlling the BLDC motor's speed and reducing errors.

In the recent education scenario, when everything is going online, cloud technology played a vital role in providing a supporting spectrum.. It is proved that in this Covid pandemic times, online education is increasing to a large extent, and cloud computing technology is the backbone of the online education system. There are various issues in the implementation of Cloud computing and its adoption in education, especially in the higher education sector. The student’s attitude towards using the cloud concept in its education setting requires many factors to be considered such as ease of use, the security of the cloud, trust in technology, and social norms to maintain the integrity of an individual’s data and scale of the instrument (Sandhu I K, 2020). This paper aims to develop reliable and valid cloud computing adoption measurement scales from the student perspectives in higher education institutions in Punjab through a pilot study. A fair amount of literature is also mentioned on accessing the scale for development and validation from the student’s perspective on cloud computing adoption.

Today, the Internet of Things (IoT) is very pervasive and is an integral part of human life. Subsequently, using various IoT platforms, a platy range of IoT devices have been deployed and developed. Although one of the most exciting IoT technology innovations gains interest, there are still concerns about its security. This paper gives the glance of different cycle of platforms and evaluate six of the most widely used IoT platforms (Azure, AWS, IBM, Google Cloud IoT, ThingSpeak, Thinger.io). Various sketches have been applied and tested in similar conditions and environments, as well as we evaluated how the platforms perform regarding the current criteria. Besides, problems and gaps in IoT platforms are discussed.

Crime analysis and prediction is a systematic approach for identifying the crime. This approach can predict areas which have high chances for crime occurrences and graphical presentation of crime areas. Using the concept of Machine Learning using K-Means algorithm. By using the available data, we can extract useful information by forming k number of clusters. Through which we can build Machine Learning models which helps us in prediction. Due to increase in the crime rates and density of the population rapidly quality of the people’s lifestyle is decreasing, which leads to decreasing the reputation of the nation. So, there is a need of securing the lives of people. Hence, there is need a advance of technology through which we can predict the crimes accurately. The proposed system helps in analyzing data and helps in accurately and thereby the crime investigation people and the cops can predict the crimes and can control the crimes.