Search

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

5th Industrial Revolution is taking place in the present world. The most appealing theme of the new revolution is the dominant role of humans. The humans as protagonists would innovatively use digitalization based technologies for the benefit of economy and society. For any revolution to materialize, it takes cumulative efforts by industry, government, educational institutions, entrepreneurs and society. The establishments can act as catalytic agents, by channelizing the investiture in the direction of the new revolution, to enable the smooth transition. In the 5th Industrial Revolution, the spot light is on the humans. The agents by refining the humans can bring in the new revolution fast. In the new revolution, humans would creatively work along with machines and this joint performance will ensure speed & accuracy on the one hand and personalized output on the other. To compete globally, India has to be ready for the upcoming revolution. The idea of active involvement of the humans does agree with the fact that India is a very populous country and currently at developing stage. The argument suggests that the new revolution is going to be boon for India. For India, it would mean a better way of earning and a superior standard of living. The current paper throws light on the catalytic agents which would lead to the smooth transition from 4th Industrial Revolution to the 5th Industrial Revolution. The paper also propounds a model which would fasten the pace of socio-economic development to achieve the new revolution in India.

In this time of the pandemic we all are fighting to stay safe and work side by side and we all have one question in our mind and that is when all this will be over. In this paper we have proposed the SM Model of data prediction. This model can project the future growth of COVID-19 in the states of India and Union Territories by using machine learning in python3 and makes use of the data collected about all the Indian states and union territories. This paper makes use of data collected from various sources to make sure that the proposed model gives us a relatively low margin of error in the growth prediction.

News is basically information, information regarding recent or current events. There are various platforms or sources of news like: print media, TV channels, digital media, social media and films. It is obvious that news floating in these platforms are not always correct. Sometimes these platforms contain false or incorrect information, which is termed as fake news. Fake news can be in the form of images, videos, audios and texts. Generally fake news is generated due to a machine or human intervention. In order to identify fake news in various platforms this paper reviewed several techniques based on a machine learning and artificial intelligence. This paper is going to review various machine learning based approaches and some of the models are Naive Bayes Classifier that has been tested in a software system with a data set of Facebook news posts and had achieved an accuracy of 74%, Convolutional Neural Network (CNN) for image visualization scored a mean accuracy of 92.85%, Recurrent Neural Network (RNN) for audio and text visualization had achieved an accuracy of 93 % on the datasets collected from Brussels terrorist attack in 2016. Some of the hybrid models are like for the early detection of false news on twitter using propagation path classification using combined CNN and RNN model had achieved an accuracy of 85%, Meta Optimization Semantic Evolutionary Search model (MOSES) scored a mean accuracy of 63%, Capsule Neural Network (CapsNet) model for an image, audio, video, text mining scored overall accuracy of 99.8%. Total twelve machine learning models have been represented with proper data visualization of their accuracy percentage and found that Artificial Neural Network based Capsule Neural Network (CapsNet) model is the best with a highest accuracy of 99.8% over LIAR and ISOT set of datasets.

In this paper, we study how to assemble a supply chain with the avail of Blockchain to establish a secure trading environment. Blockchain is distributed, decentralized and a transparent mechanism which makes it suitable to share data on publicly accessible networks such that it can further be verified and audited independently thus maintaining data integrity. This paper suggests that the Blockchain technology offers great potential to foster secure trading using distributed ledger that propounds ingenious platforms as it makes the history of any digital asset unalterable, traceable and transparent. We further study how Blockchain mitigates the subsisting quandary in supply chains, how does it provide an edge over other technologies. The model is proposed to implement the Blockchain manufactured supply chain and it is further explained how it eliminates the challenges faced by the current supply chain management system. Also, challenges to adopting this technology have been discussed in the latter section of the paper.

At present era, digital technology plays an important role in computer education because of its important role in various segmentations, due to lack of support and application of digital technology, education may not success in to an eye catchable performance. In short, the primary goals of this research paper are to offer a fresh take on the application of digital technology in the context of transformational changes and to provide ground-breaking insight for the important researchers, scientists, teachers, and readers who are curious about the future of computer education. We the researchers have focused on a valuable and up-to-date literature review of the aforesaid research title. We have tried to our level best to justify the aforesaid research title in to a valuable and meaningful research work for the future researchers. In this paper, a thorough summary of the literature on how digital technology contributes to computer education is provided. However, this paper aims to identify many important emerging issues in education technology and major obstacles hinder broad successful implementation in the area of computer education, even though “technology in almost every element of education” are widely used. This study also found similar results, however here the paper is concentrated on the additional difficulties that were discovered due to the advent of the digital revolution to improve the empowered use of digital technology on education across all sectors, digital transmission must be applied to achieve this.

Despite the world being in 21st century most of the developing and under-developed nation use traditional method for farming we requires tremendous energy and hectic schedule from a small scale farmer with a very measly return in terms of profit moreover the water wastage and continuous monitoring required to keep check in plants condition is just unjustified but with 58% of population having agriculture as primary income source most of the Indian farmers having extremely low income it seems impossible for them to hop over costly machineries. But now with the cost-effective processors out there in the market can provide a solution to all these issues faced by Indian farmers. With exponential progress of Internet of Things (IoT) devices in the market smart irrigation systems are becoming a new trend. This paper proposes design and theory of one such smart irrigation system using NodeMCU to wirelessly operate a network of irrigation modules by irrigating the field when required by measuring the water content of soil and keep checking condition of plant using a camera this paper also provide insight of how to keep safe integrity of data which travels from NodeMCU to user smartphone using ciphering methods and by keeping proposed system reliable and cost effective.

The paper represents penetration testing Industrial Control Systems (ICS), and its operational working along with systems, that uses the Modbus TCP/IP Protocol and PROFINET Protocol. Modbus has many vulnerabilities that are explained in this paper and an analytical correlation on how these susceptibilities can be exploited through specific attacks. Further, a comparison on alternative protocols, i.e., PROFINET, is provided to show the fortes and flaws of using both of these protocols in ICSs. The paper reflects that Modbus is obsolete and far carry out risk in ICSs without unambiguously configured extenuations put in place. Further, the paper reflects research on this technology and presents in a systematic way to understand the knowledge in a new area of cyber security.

High mortality rate among women in India is mainly due to breast cancer. As mammography is a standard imaging tool used in screening the breast, it sometimes tends to miss certain abnormalities that lie beneath the breast regions. Thermography being a temperature-based functional imaging modality has enough potential to identify the abnormal changes in the breast through asymmetries at an early stage. This paper considers breast thermograms of patients that are normal and abnormal wherein pre-processing is applied to extract the ROI. The pre-processing includes breast region segmentation using horizontal and vertical projection profile approach along with the Otsu method. The features are then extracted from this ROI for which they are also fed as input to the non-linear classifiers for classification of normal and abnormal. The outcome of the paper results in a good accuracy rate that can be efficiently utilized as a Computer-Aided Detection tool for a second opinion in the breast cancer diagnosis.

Making a computer understand the images is the task of computer vision. However, to familiarize it and learn to analyze what the computer is able to perceive is a tedious task. To address this challenge, Convolutional Neural Networks using deep learning frameworks could be performed and our goal could be achieved. For enhancing this task and making a computer learn more accurately and precisely about the variations in the real-world images, augmentation is an excellent way for making it possible. In this paper different types of normal augmentations, using deep learning methods for augmentation and other important works on this technique are discussed. This paper tries to cover the critical features of the augmentation, which will thus not only aid the new researchers but also other experienced researchers to interpret the latest trends going on in this technology, this would enable us to work together to make the computers learn more meticulously by utilizing various deep learning models that are trained on the images, making them more efficient and robust.

Serving content to the worldwide distributed clients with high availability, high efficiency and minimum delay is the primary challenge for today's virtual world. Content delivery network (CDN) was evolved with the aim to send the data at the client's doorstep with almost zero latency. CDN disseminates the content at various edge servers deployed to the client's close proximity. But locating the positions for deployment and also sharing the load among the servers are two major concerns for CDN. In this paper, unsupervised K-means clustering is considered for selecting the locations for server deployment which also considers the under populated clusters based on a parameter called population threshold. This paper introduces a hybrid load sharing model which is mainly concerned about the traffic coming from the clusters with population lesser than the threshold. The result of the study shows that this hybrid approach enhances the server utilization factors of the surrogates deployed in the network while minimizes the server maintenance and cost overhead.

Intrusion Detection System (IDS) is basically a system that monitors the system or network activities and detects the occurrence of malicious activities. Intrusion detection is important as it plays a significant role in developing an efficient system. Anomalies need to be identified as they can lead to the root cause of an issue or mishap by analyzing the data and also reduce the threats and vulnerabilities to the system. Machine learning algorithms work better as they are adaptive and can give time analysis and insights into the test data. The algorithms can be trained effectively for multiple data and can track the exploitation of the system. Numerous approaches can be followed for intrusion detection. This paper discusses and compares four major supervised machine learning algorithms and how they are effective in various test conditions. The paper aims to create an Intrusion Detection system comparing various ML techniques to distinguish between the normal (good connections) and the intrusions (bad connections), to recognize the best fit technique on which the further developments can be carried.

With the ascent of data innovation, and it has proceeded with coming into the human services division, the instances of diabetes just as their side effects are reported. Examination in the distinction of regression strategies depends on specific parameters discovering answers to analyze the illness by investigating the examples found in the information through regression models. The paper throws light on the diabetic records of pregnant women. In this paper, linear regression and logistic regression calculations have been utilized on a pre-existential dataset to anticipate whether diabetes is recorded or not in a patient. Results from both the calculations have been analysed and introduced.

Millions of people around the world make use of smartphones to be connected to the internet and use social media. The use of social media sites like Facebook, Instagram, WhatsApp, YouTube, Snapchat, Twitter, Linked-in, Tik-Tok, etc., And social networks connect up to trillions of data are collected and marketing agents are using that data to promote their market. Well, we analyze these data for this paper and the challenges and difficulty involving in the specific data analysis network. Online media offers an enormous pool of purchasers ready for brand correspondence. however, web-based media is not always about brands. It's about individuals offering their lives to others they know dependent on regular interests and they despise interferences, especially when somebody is attempting to sell them something. Thus, a brand-driven center can be perilous. Social media plays a vital role for youth. In this paper, we also discuss the influence of social media on young people.

Translation of multiple images or documents into editable text, that can be decomposed is termed as OCR. Different automated machine learning tools have been used in the literature that segregate the data into characters. This automatic segregation helps to convert the copies into electronic format. This paper deals with multiple feature extraction techniques and also the data labeling tools that have been used extensively for character recognition. This paper presents the present state of art techniques to provide further direction of research for the researchers.

In the future, the majority of vehicles could be intelligent and autonomous. In order to achieve this, the 6G will play a predominant role in vehicular technology. Moreover, the next generation 6G wireless communication is an emerging technology that will revolutionize vehicular technology. In future, the new 6G technology is expected to connect more devices in our world. So, it needs to provide excellent performance over recent state of the art communication technology like 5G. The primary features of 6G technology are heterogeneous object connectivity, low power consumption, high throughput, high data reliability, ultra-low latency. Furthermore, it will provide intelligent and secure communication with the help of Artificial Intelligence (AI) and Block-chain technology. First, this paper presents the introduction of 6G technology. Later, we discuss the 6G enabling technology, which includes Internet of Everything (IoE), AI and Edge intelligence. Then, this paper mainly concentrates on how to help 6G based vehicular communication in various vehicular applications. Finally, we address the various research challenges on 6G based vehicular communication. Thus, it will be helpful for future research to carry out in the right direction.

Forest fires are one of the greatest risks to wildlife as well the human economy and the cases of wildfires have increased in the previous several years. The main causes are global warming and ongoing human intervention with natural environments. Automated forest fire prediction systems can lessen the harm caused to forests by detecting fire in the early stage and sending alerts to the concerned team so that appropriate preventive measures can be taken timely. In past few years, vast research has been conducted in this field while implementing various methodologies such as classification, clustering, supervised and unsupervised learning and so on. In this paper, the comprehensive review of such techniques is presented which can help users to identify the prediction systems based on its accuracy. This paper also presents a detailed comparative analysis of various existing techniques in terms of accuracy of the prediction systems.

This paper proposes a framework that will detect different types of traffic signs from images for Traffic sign recognition and detection in advanced driver support system. In this paper classical LeNet5 network model is used with GTSRB dataset for evaluation purpose. Shape and color are the two main factors that distinguish road signs from other objects. Firstly, preprocessing of an image to highlight important information. Secondly, the training data are scaled, normalized, expanded, and augmented. Finally, the validation and testing phase in which traffic signs are classified and accuracy is calculated. It is concluded from the results that our system has achieved greater accuracy due to the architectural optimization of the neural network for detecting traffic signs. Significantly better performance was achieved with partially blurred images. Traffic sign are classified in 43 classes. For training 22271 images are used in which 6960 images used for testing and 5568 images are used in the evaluation purpose. With new design model 98.75% accuracy is obtained.

In 2020, a virus commences generating headlines all over the globe due to its unprecedented transmission from one person to another person. COVID-19 (stands for corona virus pandemic 2019) has been responsible for the thousands of deaths across so many countries such as Italy, America, India, Brazil, China, etc. No one is saved from this pandemic around millions of people gets affected by COVID-19 across the globe. Although, many elements are get affected by this and one of them is wearable devices for example smart watch, smart bracelet, smart shoes, a smart belt, etc. In this digitalization era, wearable devices are standing to face with the present COVID-19 pandemic. Hence, in this paper, we discussed about COVID-19 pandemic role behind its growth in the market, and what was its impact? This paper introduces the applications of wearable devices and provides a brief understanding of how does it play a role in fighting against the ongoing pandemic. These devices accredited remote health monitoring, screening, real-time tracking and better treatment without transmitting the virus from one person to another person, and infected person. Thus, it decreases the burden on the healthcare industry for protection and reducing the transmission of corona virus. Millions of human beings suffer from a respiratory illness that requires continuous monitoring to tackle this wearable device helps hospital staff to keep track of the breathing parameters of patients with corona virus and provides solutions to retrieve the patient's vital data with chronic disease. Hearing the news of growing patients affects the mindset of the rest of the people which increases stress level and anxiety. To overcome anxiety and stress level, wearable devices that tracks a person's heart rate variability (HRV).

In last few decades one of the most emerged technology is mobile networking technology. Mobile network innovations have acquaintance with different ages of advancement, specifically from First Generation to the Fourth Generation. Current research in this domain is concentrating on cutting edge execution of 4G and introducing 5G technology. The Fifth generation (5G) technology will reconstruct the path of usage for majority of users. With 5G technology users will expertise a new level of speed in data transmission and calling. In this paper, we have made an approach to study the actual generations of wireless mobile technology. This paper portrays the evolvement of varied generations of mobile network as well as the importance of one over another. This new generation of technology embody all kinds of modern options that create 5G technology paramount in near future.

the agile software development process has a set of standard agile ceremonies, which are must for an agile team to conduct in order to preserve its agility. However, there is always a challenge for agile teams to decide how much agile sprint time should be spent on hosting agile ceremonies and agile product build tasks. This is due to the fact that if most of the agile sprint time is spent on hosting agile ceremonies then the time for agile product build tasks will be reduced significantly impacting the velocity of the agile team. Hence, there is a need to find a solution with the help of which agile team can strike a right balance among various agile ceremonies and agile product build tasks during a sprint. To overcome this gap, this research paper analyses the data for 14 agile sprints to understand the agile time distribution across agile sprint ceremonies and agile product build tasks under current approach. Because of which, this research paper proposes the values for two newly introduced agile co-efficient namely coefficient of agile ceremony time and co-efficient of agile product build time to suggest the ideal time to be spent on agile ceremonies during a sprint. From the research results, it was evident that the velocity of the agile team following the proposed approach reported an increase in velocity by 13.96% for sprints when compared to the velocity of the agile team following the existing approach. The research work also highlighted that the duration chosen for agile ceremony by agile teams is independent of sprint lengths.

Agriculture crop prediction is a challenging and interesting problem domain for the computer community. The computer machine helps to improve the productivity of the crop with such predication. This paper illustrates a research model which provides the predications of the wheat crop in the Rajasthan region of India. This paper uses feedforward neural network models to accomplish the task of predication for the wheat crop. These models are trained with three different feedforward neural network architectures namely multi-layer perception feedforward generalized regression and radial basis neural network. There is feature scaling method was used for normalization of large previous year's crop detail data which has been obtained by agriculture department of Rajasthan government. The simulation results indicate the better prediction with 97.75% accuracy found by the feedforward neural network model trained with backpropagation learning rule in comparison to Generalized regression neural network and Radial basis neural network.

Offline Handwriting Recognition, allows to recognize and convert physical files and images to a digital form. Handwriting recognition is a vast area of research and a lot of contribution has been witnessed in past decades. Devanagari Script is one of the scripts for which many sophisticated handwriting recognition systems are not available. Deep Learning techniques, however, have been able to generate excellent results and enhanced performance. This paper thoroughly examines the recent advancements in deep learning architectures to recognize Devanagari Handwritten characters from varying datasets. The paper has been focused on Devanagari Handwritten Recognition Systems background, challenges, opportunities and future research trends. Several publicly available datasets have also been mentioned along with their data size, type of characters. A comparative study has been done to present the results and insights of the state-of-the-art approaches.

A metropolis is said smart when diverse areas like homes, health care, irrigation, parking system are fully automated. Among these areas, Parking System plays a pivotal role because inefficient parking results in serious problems. Driving around for a parking spot after arriving at a mall or a multiplex is nerve-wracking. An effective and efficient solution to this is a Smart Parking System designed using IoT and Cloud storage. This design supersedes the routine concept of parking system by exploiting IoT technology and combining it with the latest developments such as IR sensors, Ultrasonic sensors, and microcontrollers that collects information about the availability status of parking spaces of different areas. Hence drivers can search for the nearest available parking slot with the help of a customized software application and web page. The main motto behind this idea is to reduce the time taken to search for the parking area. This paper presents novel ideas like sending the average occupancy data to the respective owner to decide on expanding the parking area, detecting the vehicles parked at No Parking Zones. This paper also includes a prototype designed and implemented in real-time.

In today's world, security is based on the manually monitored surveillance cameras used at various places. Since, human based monitoring is very tiring and difficult task, this raises the demand for automatic system to detect anomaly at crowded places. It involves understanding crowd behavior, their interaction/collision with each other, individual speed, crowd density, different classification methods to identify anomaly etc. This paper studies the different techniques used to study crowd behavior, individual and crowd characteristics. It also compares the results of various methods used in survey papers for common datasets.

With the increasing emergence of social network applications, people now use social media to share their opinions on daily issues and analyse people's interactions and reactions toward daily activities such as buying a product, watching videos, community services and other viral situations. A significant number of children watch videos using web platforms such as YouTube. However, such platforms can contain useful or harmful videos to children. Many attempts have been conducted in order to rank the quality of YouTube Videos based on the sentiment analysis approach. However, few attention has been given to Kids' YouTube videos. Therefore, this paper investigates existing methods and techniques that concentrate on YouTube video sentiment analysis. The methods classify them based on machine, deep learning approaches. This paper can be beneficial to many researchers in data mining, sentiment analysis area and more related research areas.

The world of Cyber Forensics is often filled with gigantic amounts of information, often more than what you would get from engagements in other branches of forensics. This not only makes the engagement much more thrilling for forensic experts, it also makes it much more tedious and a huge time-consuming factor when it comes to analysis. There are several tools available both from the open-source community and private devs, but not much from the fields of Artificial Intelligence (AI). Deep Learning, being at the core of Artificial Intelligence, will provide us with much better and more refined processing and predictions based on the available data. The setbacks and the breakthrough of using Deep Learning in Cyber Forensics are more or less the same as in every other branch, where AI is used to solve tasks critical to a person, or most of the time, crucial to an organization. To start with, for Deep Learning to be integrated with the fields of Cyber Forensics, i.e., after an incident, it must also be trained in the areas of Cyber Security, or to be exact, in Cyber Defense, i.e., before an incident. This idea is pretty intuitive. This paper looks at Deep Learning models as much similar to the most complex structure in the known universe, the human brain. After all, these models have been inspired and based on the human brain. This paper attempts to find existing solutions on how to best implement a Deep Learning model in the fields of Cyber Forensics and proposed how Deep Learning models could help the world of Cyber Security, especially for the IR teams.

This proposed work is to review and inspect for existing innovative Black Box for vehicle ideas. This review paper will review these ideas dividing by their specification, size and compatibility. All the reviews of researchers done recently will be discussed here broadly. The techniques mostly used for black box previously were EDR (Event Data Recorder) and ADR (Accident Data Recorder). After more researches, more techniques were added with GPS (Global Positioning System) and GNSS (Global Navigation Satellite System), SMS (Short Message Service) system, Arduino controller, RPI (Raspberry Pi Controller), GSM (Global System for Mobile Communications) and many more. These techniques are used to detect the location of the vehicles; if accident occurs it will detect the cause and keep the records how it occurs, it will also send the exact location to the nearest hospitals or police stations to deduce deaths and save more lives. Most of them can be used in any form of vehicles. After reading this paper, the reader can understand about the protocols, protocols, prototypes, methods and techniques used till now for a black box to prevent accidents and also prevent deaths occurred by accidents

P2P(Peer to Peer) lending is the process of extending credit to unknown individuals through digital platforms. Trust is an imperative component for P2P lending. Blockchain and smart contracts have the potential to accelerate the growth of Peer-to-Peer lending as trust-inducement and decentralization are the two prominent features of blockchain. Lending on social platform is particularly unsecured because they are not backed up by collateral. In this paper, a token generation mechanism is proposed in lieu of the submission of digital assets and virtual currency as collateral. The paper also deploys the self executing smart contracts to allow investors to lend money to borrowers on pre-determined terms and conditions. In addition, a smart compensation management contract has been deployed to repay the outstanding amount to lender if the borrower has charged off.

Blockchain is an emerging technology which works on decentralized peer-to-peer network and allows to make transactions securely by eliminating the need of a central authority. Blockchain is renowned for exchange of crypto-currencies but with the advent of smart contracts it is now widely used in other applications such as smart supply chain, healthcare, smart transportation, online voting, public donations, will management, buying and selling property and insurance sector. The smart contracts are digitally signed and make applications immutable and transparent. This paper is aimed at unveiling Blockchain technology, smart contracts and its applications. This paper is also disclosing the barriers in adopting Blockchain and smart contracts. The recent literature was reviewed to find the answers to the research questions. Huge power consumption, slow transaction rate, complex architecture and lack of awareness and expertise are identified as the barriers in Blockchain adoption whereas legal issues and high transaction cost are the main barriers in adopting smart contracts.

Fog computing also referred as “fogging” or “fog networking” through which a decentralized computing structure is explained that is situated among the data producing devices as well as cloud. In logical locations, users are enabled for placing resources that include data as well as applications produced by them, with such flexible structure for enhancing the performance. The fog computing involves edge computing, cloud's power as well as various services which are brought closer to location where data is produced as well as processed. The terms edge computing and fog computing is utilized by various peoples interchangeably, as the primary aim of both technologies involves bringing intelligence as well as processing closer wherein data is developed. Generally, it is performed for enhancing efficiency, however, it can also be performed for compliance and security reasons. This paper explores the different concepts involved in fog computing, importance of fog computing and highlights various opportunities in the fog computing area. Paper also presents the key points that why this technology needs to be successful. Finally, it addresses various issues, challenges in fog computing that it should deal with.

Corona Virus (COVID-19) outbreak in December 2019 had a devastating effect on the world. The impact of the coronavirus epidemic has not only affected economic activity but also human life. There is exponential growth in machine learning technique for analyzing the medical data. Therefore, predictive analysis is performed using various algorithm to showcase the impact of Covid on different parameter of economy. This paper aim to assess the overall impact of COVID-19 on the Indian economy through digital learning. Statistical data from reliable sources were collected to determine the impact of Covid on the Indian economy. With this the actual difference between the asper count and the actual number can be determined and can be considered as a reference for future. In this paper, hybrid of L1 regression and tree based algorithm is contributed for optimized feature section process. Further, KNN is utilized for classifying the target values. The different performance metrics are computed on model, thus achieving accuracy of 98%, precision as 81.5%, Recall as 99% and F1-score as 92.5% respectively.

A multi-band microstrip patch antenna with truncated corners for various wireless applications has been proposed in this paper. The antenna consists of a rectangular patch having dimensions of (29.8*38.4*0.03mm3). The patch is mounted on FR-4 material, which is used as the dielectric substrate. Multiple bands are obtained covering frequencies from 2.4-2.5GHz, 2.9-3.0GHz, 4.3-4.4GHz, and 4.5-4.7GHz supporting various microwave applications. The first resonant band is used for the IEEE802.11(WLAN-Wi-Fi), IEEE 802.15.4 (LR-WPANs) standards in the SM band. The second frequency band is used to operate unlicensed Part 15 Devices in the AM and FM radio broadcast bands. The third resonant band is used for radio altimeters, satellite, and cordless telephones. The fourth resonant band is helpful for military applications and fixed satellites. The antenna design and simulation are done using CST microwave studio. The simulated parameters such as S-parameters, VSWR, radiation pattern, etc., are observed for the proposed structure. The simulation and the measured results are obtained and compared and presented in this paper.

There has been tremendous distress over the globe since 2019 due to the novel Corona Virus. The COVID-19 disease has caused the medical services a great deal of pressure and a medical emergency all over the world. Very quick dissemination of coronavirus has muzzled as well as endangered human civilization. Various processes have brought attention to the medical propensity for the diagnosis of Corona Virus disease. Antigen and Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests have been used since the brisk emergence of COVID-19. The emanation of various mutants developed in these two years has put some questions on the precision and accurate result of these methods especially during the second wave of the pervasive coronavirus. So, various radiological methods like Computed Tomography (CT scan) and X-ray, etc. have been utilized for the spot because of the more meticulous result. The investigatory study in the given paper is about the application of Artificial Intelligence in speculating the coronavirus disease using a suitable model. The research carried out in the given paper gave an accuracy of 99%. Various machine learning-based classifiers are being adapted for the prediction of the disease.

Artificial Intelligence (AI) is practically apply in all commerce and notable labors be able to witness in our routine life. E.g. chat bots, smart cars, IoT devices, healthcare, banking, and logistics and many more. Simple examples for the use of AI in day to day life are Alexa and Siri which are ready to assist to ease the work. They can be illustrated as consumer Artificial Intelligence powered smart assistants. They are of help that requires unskilled tasks of an individual's life, thus one be capable of centering significant stuff. But, have we ever thought that AI can infiltrate the limitations of the by hand encumbered legal processes as well? Can it help professionals and advocates with mechanization, by sinking their time on repetitive responsibilities? Taking about why and how AI and law connects, the prime reason for the same is to craft inventive applications to computerized routinely procedure or working related to legal matters. Artificial intelligence and the law cannot go hand in hand that too with synchronization. There could be many reasons, on among them could be immensely diverse attribute. In the current era, almost every legal practitioner is making use of Artificial intelligence in their day to day carry out procedures in which e-courts and use of software's are common which helps them to complete their responsibilities effectively and efficiently. This paper provides a general introduction to the field of AI and Law. This paper aims to introduce to the way in which the increasing use of artificially intelligent technologies affects the law.

In this paper, a wide literature review of soft computing techniques in intrusion detection in V ANETs is carried out. The soft computing techniques used by different researchers for carrying out intrusion detection in vehicular networks are examined thoroughly. Summary of different approaches used in intrusion detection are thoroughly discussed. The study will focus on the techniques implemented by researchers in literature for developing the intrusion detection framework using soft computing approaches including, deep learning methods, artificial neural networks, fuzzy logic, and other machine learning methods. The paper also provides the performance analysis as well as the limitations for the reviewed works.

Cervical Cancer (CC) is the fourth most frequent disease among females. According to a 2020 report from WHO, approximately 6,04,127 females identified with cervical cancer worldwide and about 3,41,831 females estimated under the death cases from this cancer. The main purpose behind this paper aims to provide a review and comprehensive overview of the automatic segmentation and classification of DL algorithms, and also offers a description of their architectures in the cervical cancer image analysis from Pap smear images. The research articles are considered from publically available scholarly sites such as Google Scholar, IEEE, Science Direct, PubMed, Springer, and Elsevier on cervical cancer medical image analysis. Only those papers are included that are related to the detection of cervical cancer with DL algorithms. Each review articles are considered using three sets namely: Set1- Segmentation, classification, and cervical Cancer; Set2-Medical imaging, Machine learning, and pap-smear, and Set3- Pap Smear images, Automated System, and Classification. Most of the existing techniques achieve an accuracy of 97.89% on publicly available pap smear datasets (Herlev Dataset). In the conclusion, research gaps and limitations are mentioned that reduced the classification accuracy. For reducing the limitation, also discussed the recent advance and future perspective concerning cervical cancer detection.

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naive bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

Today, almost billions of people use social media sites to share pictures, thoughts online and connect to people worldwide. During this challenging time of pandemics, we are leaning towards social media more than ever, increasing our engagement. It has made us aware of the coronavirus cases worldwide, has entertained us, helped us stay connected to our acquaintances living far away, and even helped many to start and grow their small businesses online. Besides the wide range of advantages that social media offers, it also comes up with many disadvantages of being an online platform. Issues like fake profiles and impersonation have increased on social media platforms, such as computer-generated bots, human-generated, or cyborgs. Such accounts are made with malicious intentions. Moreover, there is no feasible solution to such a problem. The paper aims at developing a model which can detect fake profiles on social media by using machine learning techniques for better prediction and identification. For this paper, Instagram data is considered for the availability of the dataset, and the analysis will be done by implementing the machine learning algorithms, which gives the highest accuracy on the dataset. By accurately detecting such profiles, social media platforms can be made safe for users.

VANET as Inter-vehicle communication is becoming a promising area of research, standardization, and growth with the ever-growing number of vehicles equipped with computer technology. There are several setbacks or challenges in VANET that must be addressed in order to provide a reliable service. Network architecture, routing Protocols, and VANET security are some of the issues that need to be addressed. Safe and secure routing in the VANET is a major concern due to the high mobility and changeable infrastructure. To conquer this, we demonstrated various routing protocols in this paper that can help address these concerns in VANET systems, as well as a comparison of position-based and cluster-based routing protocols in this paper. Due to the Cluster Head selection, cluster-based routing is more feasible and reliable than position-based routing. This article discusses which protocol is better suited for VANET.

Internet of Things (IoT) is a scientific advancement that is likely to transfigure our traditional way of life by automating everything connected to the internet. Smart physical devices/objects have become indispensable part of our lives thereby making our being simplified and way more organized. Internet of Things encompass and empower several heterogeneous physical devices/objects for sensing and gathering information from surroundings, consequently exchanging it in a seamless mode to realize smart application goals without human intervention, for instance, home automation, smart cities, smart agriculture, and smart industries etc. The growth in the number of such wired/wireless physical devices/objects and the speed of that growth raises concern over security. To be able to implement a smart world with such billions of physical devices/objects, appropriate mechanisms are required to provide secure end-to-end communications amongst them. In addition to it, a variety of attacks may occur in various layers of Internet of Things. Without a well-founded and congruous Internet of Things environment, budding Internet of Things applications cannot be exploited to their full potential. Hence, reliability, security and recovery from attacks are amongst the most unsparing sides of the Internet of Things. In this paper, we have presented a comprehensive review of Internet of Things security issues followed by security measures in Internet of Things. The focus in this paper is also on research challenges and application domains of Internet of Things.

The smart city landscape is experiencing highly transformative and innovative approach for sustainable development of urban areas. Therefore, a systematic review was performed based on a search of the existing literatures, which helped us to understand the relevance of emerging technologies for enhancement of sustainable transportation and mobility in smart cities. This paper provides the most extensive analysis and preliminary evidence of the impact of emerging technologies on future mobility and social life in smart cities by 2030. The paper focus on the two main issues, first is, how the emerging technologies can reshape the future mobility and second is how these technologies can enhance the overall experience of user. Overall, these findings suggest that emerging technologies are promising drivers for future mobility in smart cities.

Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. Over the last decade, data has increased exponentially in networking domains, so naturally, there is a need to understand this data to get useful insights. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute to the outdated KDD'99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random forest, Extra trees, AdaBoost and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for a comparative analysis among all the classifiers used. This analysis gives knowledge, investigate difficulties, and future opportunities to propel machine learning in networking. Along these lines, it is a beneficial contribution to enhance the understanding of machine learning for networking, pushing the limits of automation using machine learning for better network management. This paper can give a basic understanding of the data analytics in terms of security using Machine Learning techniques.

Ambient Intelligence plays a vital role in developing assistive technologies like smart homes, aiding disabled and elderly living, and emergency assistance and learning systems. As an evolving applied science criterion, Ambient Intelligence is taken to the next level by considering behavioural and humane aspects into day-to-day applications such as traffic, education and smart homes etc. Although recent progress has indicated significant breakthroughs in computational and cognitive directions, the Human-Computer-Interaction dimension is not effectively applied. Interaction principles such as accessibility, usability and learnability from the user acceptance aspect are not significantly explored. The embodiment of these principles can lead to effective usability by a potential user. Through this paper, the author would like to identify the significance of usability towards user acceptance for qualifiable ambience in intelligent environments. This paper presents the perspective-based reflection of the potential and younger user group validated against the correlation of user's educational backdrop and experience towards technological acceptance. Also, a novel philosophical/policy level phenomenon to determine the principles through which usability in such environments can be made acceptable is discussed.

This paper present a framework and implementation of automating the escalation and prioritizing system to handle the request and solve them based on their priority. This paper use the Microsoft office 365 tools to design and implement the system. This paperdiscuss the unattended robotic process automation in achieving the email ingestion, triggering actions for performing the fetching and extracting the value. Power automate is a tool used to perform the desired task for escalating and checking the priority of a given request. Based on the prioritization rules the SharePoint list get updated for a particular booking number or new ticket get created.

Weather forecasting which is a key player in everyday life is a remarkable advantage of science and technology. Weather prediction can support people as protecting the assets and lives of them. The persons involved in outdoor occupations can be benefited by the weather prediction as they needed to know the weather previously. In the paper, the concept of supervised learning is used, which is one of the learning techniques in machine learning. The paper presents the algorithm, which applies the concept of linear regression and artificial neural network, to predict the variation of temperatures such as high and low values as features in a linear combination. The main features focus on maximum or minimum temperature, mean values of humidity, and atmospheric pressures considering the previous two days. The paper focuses on prediction and analysis of weather forecasting which further entirely applies the concept of machine learning.

The present era of digitalization has resulted in enormous amount of data lying on the web which demands in quick and easy access to the relevant information. Search engines are the powerful platforms that process users' query sent in the form keyword or a phrase to locate a particular information stored in their huge databases. But the extensive range of sites make it a tedious and endless job to extract the most significant information. Progressively search engines started adopting certain criteria such as quality of the content or keyword analysis to rank a website in the organic results. Since branding is the pillar to business success, companies and professionals are competing to make their website superior from each other's. This led developers to seek out and analyze the search engines and it's working to rank their websites on the top. Gradually it was known that Search Engine Optimization (SEO) is the mechanism that determines these factors and affects the ranking and visibility of a web page on search results. This paper aims to review the literature of previous studies and research to help readers gain an insight and retrieve necessary information about SEO and its practices. A part of the paper also refers to the general quality of a website in perspective of SEO. To aid developers and site owners, we complied list of best SEO techniques that can leverage organizations for improved brand development.

This paper provides an in-depth review of such Robots and their prototypes that were developed to achieve a motive of detecting and picking trash. The varied approaches of path-planning and Trash detection with the help of image-processing and object-detection using machine learning/deep learning like YOLOv3, SSD-MobileNet, AlexNet-SSD etc. followed by different papers are elucidated in this paper. There was even implementation of ROS in some articles for map segmentation.

This study will give the detail understanding of Augmented Reality and the existence of Augmented Reality in the education sector. This paper will also be contributing in understanding that how much user's and teachers are aware about this technology and how many of them have experienced the technology. This study is also giving review on implementation of AR technology and how this is giving benefits to users in different subjects and at different ages as compare to traditional mode of learning. Paper will also help readers in understanding about the perception of users and teachers if they have experienced AR in learning, teaching simultaneously and further scope of research with certain limitations.

In this paper, a brain tumor segmentation method has been proposed. Image segmentation is required for the detection of brain tumors. For this purpose, brain images are divided into two distinct areas. This is one of the main but also the most complex element of the tumor identification process. A brain tumor is a result of mass of tissue that grows, it is the most important cause of the increased mortality rate among children as well as adults. This paper proposes a brain tumor segmentation method by using a combination of Adaptive Histogram Equalization and U-Net architecture. The performance of U-Net architecture and U-Net Architecture with Adaptive Histogram Equalization Technique are contrast by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), Quality and Complexity parameters. Overlap-tile strategy is used to smooth the segmentation of large images. Many MR images were collected, and the observations were carried out for statistical analysis using the Adaptive Histogram Equalization and U-Net architecture.