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Writing and presenting results is important for any research work. Publishing is not enough; it should be written clearly and consistently for others to read and cite. We present a review of different techniques, for improving the writing in research papers intended for publishing. These include how to write the title, whether to use active or passive voice, how long sentences should be, how to present the uncertainty, understanding the International System of Units (SI) writing conventions, how to assign authorship, how to effectively use references, how to present the work, and how to publish it in a journal. This paper is a summary of the main and most recent recommendations in the scientific field on how to write a research paper to increase its impact.

In recent times, it has been witnessed that wireless systems based on IoT-based have developed rapidly in various sectors. The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. The WSN (Wireless Sensor Network) is a central component of the IoT, which has proliferated into several different applications in real-time. The IoT and WSNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WSN nodes are usually small and battery-driven machines. Thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in IoT-WSN systems were presented. This paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

In recent years, agricultural wastes cause serious pollution to the environment. Agricultural residues can be used to produce value-added products in order to control environmental pollution, therefore these wastes are a promising feedstock as it has many advantages such as wide range of source, low cost and renewable. Rice straw and lemongrass leaves which are the renewable sources for the production of many useful products such as xylose. The aim of this study is to compare the content of xylose produced from rice straw and lemongrass leaves, in order to obtain highest yield of xylose and to optimize the acid hydrolysis time, concentration of sulphuric acid, liquid–solid ratio and the power of the microwave towards the maximum extraction of xylose. Microwave assisted acidic hydrolysis method was used to extract xylose from rice straw and lemongrass leaves. The acid hydrolysis of both rice straw and lemongrass was performed at different time (1–5 min), acid concentration (1%–5%), liquid solid ratio (1:30, 2:30, 3:30, 4:30 and 5:30) and power (160 W, 320 W, 480 W, 640 W, 800 W). The results obtained indicate that the yield of xylose from rice straw (2.98 g/L) via the microwave assisted is slightly higher than lemongrass (2.91 g/L) with optimum conditions of 3 min of hydrolysis time, 2% of sulphuric acid concentration, 1:30 of liquid solid ratio and 320 W of microwave power. Meanwhile, the optimum conditions for lemongrass leaves are 4 min of hydrolysis time, 4% of sulphuric acid concentration, 2:30 of liquid solid ratio and 480 W of microwave power. Thus, rice straw has slightly higher capability in production of xylose compared to lemongrass.

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities; Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

In this paper Telematics and employments of Telematics in vehicles is presented. Advancement is developing every year and is used by planners of the structure in various edges for the duration of regular day to day existence. Telematics system is making in different perspectives. Apparently it gives a lot of organizations in human lives. It makes life snappier and lessening setbacks in vehicle. Telematics data gives the reason to perceiving how far, how rapidly and underneath what conditions an individual drives, similarly as a foundation for logically propelled data showing improvement. This limit is developing new and innovative things that even more correctly esteem peril and attract beneficial new customers. By then, it explains how this structure works. It is like manner tends to the favored viewpoint and block of the system. This paper in like manner fuses the issues related to the telematics structure. Finally, it will see logical examination and wrap up this paper.

Over a last few decennary free space optical communication (FSO) have become more interesting alternative to the radio frequency for communication. The FSO is emanating technic that have been found the applications in different place of shorter & longer distance communication. The any technology, of FSO has been worked that much good in the several applications rather than other. In this paper we will examine FSO from different angles. The analyses this paper accompany of the technology has shown that FSO technologies inherent strengths and it lack the use of in ground cables which make it much expeditious & cheaper to installing, and the facts that will operates in the unlicensed spectrum, it will be removed & installed elsewhere and it has relatively high bandwidth. Specifically, this paper found that because FSO uses air as a communication medium, its performance & reliability are restricted by fog, rain, snow and dust. Maximum range of FSO may be in kilometers only. The applications that FSO technology seems most suited to is clear this pap rather and short distance connection establishment, like interrelation to broadband networks backbone & backbone links bet this papered building in the metropolitan area networks (MAN) or the campus area networks (CAN).

Blockchain has become one of the most developing technologies in our country. Blockchain technology is a decentralized, distributed ledger that records the sources of digital assets. Electronic Medical Record (EMR) accommodates patient’s medical history, which has been carried out by a Physician or Doctor in a particular clinic or hospital or Healthcare center. Whereas, Electronic Health Record (EHR) holds patient’s entire health history, including immunization plans, diagnosis, lab reports, inoculation schedule, etc.,. This feature empowers the usage of Health IT in a more secure way with the amendment of blockchain technology. These records are maintained smart contract between the patient and the healthcare sector. In both EMR and EHR, the patient is the owner of his own data. This paper incorporates the features of blockchain technology to provide a collaborative work on Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) and it concentrates highly in diabetes as it has a sharp magnification in current world. Though it is tedious to implement this EHR all over the country, this paper works for its implementation especially in the domain of diabetes.

Mobile Ad-hoc networks (MANET) is a collection of nodes of wireless connectivity. In Mobile Ad hoc Network (MANET) all the mobile nodes are requiring restricted battery capacity. Because of the changing nature of network the sudden communication link breakage happens. Hence the possibility of data loss is more. Efficient utilization of energy and lossless transmission are the important issues in MANET. This paper proposes a new Trust based Efficient Energy Balanced Less Loss Routing (TER) Protocol for MANETS. This protocol improves the network performance by properly choosing the intermediate forwarding nodes. The Source Node picks only single of its neighbors as its next hop node by using Efficient Parameter (EP). The EP is calculated from the residual energy, distance, Occupied Queue space, the velocity of that node. The node using maximum EP is nominated as the succeeding node. This protocol reduces the number of channeling messages and hence reduces the path-finding Overhead of the network. It lessens the chance of data loss and hence improves throughput and Packet Delivery Ratio. This protocol adds trust to the selected node. In this paper, we do the simulation in ns-2 simulator and the performance is measured by with performance parameters like Routing Overhead, Packet Delivery Ratio, Throughput, End to End Delay and Average energy consumption.

The main purpose of this paper is to observe the effect PV variation of solar temperature and irradiance on different conditions and on the inverter output for a grid-connected system. Majorly temperature& solar irradiation effects the performance of a grid connected inverter, also on the photo-voltaic (PV) electric system. The simulation based study was carried out in order to evaluate the variation of inverter output with the variation of solar temperature and irradiance with the variation in climate. The analysis of Grid-connected inverter and their performance at various seasons and conditions is investigated. Solar power plant for a year. In solar power plant efficiency of inverter is also considered to calculate overall losses so, the inverter efficiency and plant performance are considered in this paper using MAT Lab software. In summer season the inverter performed efficiency is decreased because of peak temperature value and slightly increased with the increase in irradiance.

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

The present for supporting in market any industries need to bring the balance among ecological, social viewpoints and efficient. Environmental friendly machining e.g. high pressure cooling, MQL machining, cryogenic machining, dry machining is one of the latest approach which is economical and also eco-friendly that improve machinability. In present article main focus on understanding of environmental friendly machining techniques and investigate various experiences from the past writing which can help the better appropriation of ecological friendly machining approach. For study and examination of environmental friendly machining approach, roughly 100 papers are chosen. They chose articles are ordered dependent on climate of machining, year, diaries, distributor, and ecological friendly machining approach measurements. After the literature survey it was observed that in metal cutting operation heat are generated in the cutting zone due to tool chip interface so quality of surface finish are poor and tool wear. In any manufacturing industries two factors play important role in term of quality e.g. surface roughness and tool wear so in this situation some techniques are need which is environmental friendly and also improve machinability. So finally, it was observed that environmental friendly machining techniques are cost effective and eco-friendly machining compared to other convention machining approach. The main motives of this paper that studies and encourage environmental friendly machining approach.

The demand and supply gap of energy is large and the people are pressured to bear increasing hours of load losing, unnecessary intake of energy makes subjects even worse. So, the importance and requirement for energy conservation is increased exponentially. This paper outlines a step towards the conservation of electricity in standard and electricity especially with the aid of employing efficient constructing system with automation and installation of renewable based Micro Distributed Generator (MDG). The Smart Grid (SG) is a technological transformation from conventional electric grid, electro-mechanically controlled system, to smart, intelligent, and electronically controlled system. There are about 20–30% losses present in the conventional electric grid due to substandard operations at generation, transmission and distribution side. MDG play important role to build an efficient and autonomous micro-grid. In this paper importance of microgrid and some renewable energy sources utilized in formation of microgrid are discussed.

HRES (hybrid renewable energy systems) are an amalgamation of traditional and renewable sources that strive to overcome the intermittent nature of renewable energy production sources. The paper aims to provide a critical analysis of the capabilities and limitations of various software to assess HRES. The analysis cycle from design to optimization is an extremely complex task due to the many parameters being considered and has to be done thoroughly. Thus, the use of correct software to obtain comprehensive analysis is critical as it leads to minimal limitations in terms of cost, implementation, and maintenance of the system. When compared to traditional systems, hybridization can reduce costs and emissions by an estimated 20%. The software reviewed are the most widely used and comprehensive for HRES. Yet, they all come with their limitations. For example, HOMER is incapable of conducting a thermal analysis while only HOMER and IHOGA can conduct hydro energy sources analysis. As such, the paper aims to provide an in-depth review of the capabilities and limitations of various software regarding HRES, so that users can decide which software (both individually and in combination with each other) would be most appropriate for their HRES.

The concept of utilizing industrial and agricultural waste materials in concrete contributes to recompensing lacking natural resources, solving the waste disposal problem, and finding alternative techniques to protect nature. Environmental awareness regarding potential harmful effects has significantly increased, and, thus, recycling or using agricultural and industrial waste by-products has become an increasingly attractive alternative for waste disposal. Several related studies investigated the utilization of waste materials and by-products like coal bottom ash, rice husk ash, coconut shells, plastic, glass, and wood. In this paper, a thorough assessment has been conducted about the industrial and agricultural waste substances that can be effectively utilized in concrete as an alternative to cement or aggregate in the concrete mixture. This paper also aims to promote utilizing wastes by improving pozzolanic performance and material properties as part of concrete constituents.

In this research paper, our main focus is to design and develop a system for classification and recognition methodology for the acknowledgment and retrieval of a Sunflower flower in the natural environment centralized on the indigenous habitat dependent on a multi-layer method. Further, we design applications for their better classification. To handle a difficult undertaking task, an interdisciplinary cooperation is displayed dependent in the latest advancement methods in software implementation in engineering and innovation implemented by machine learning. A proposed work is design to increase the strategy for utilizing the techniques of machine learning. Final utilization of the Texture Feature, RST-Invariant Feature, Pattern Classification and furthermore utilize the K-Closest Neighbor calculations is done. Firstly, the paper is proposes to study about how to gather a flower images from the natural environment along with their corresponding background and Secondly, the paper focus on the Sunflower classification utility through Machine Learning. The computerization methods through blossom utilizing through AI system for sunflower utilized the 6-types of sunflower to get the fine yielding of profoundly sprouted sunflower blooms is caught from an advanced camera with a picture. The process of recognition implemented carried with 280 pictures. This method used a recognition as well as classification of sunflower by using the k-nearest neighbor image having overall 88.52% accuracy. This designed research paper, we trained the model with information and when concealed information is achieved then the predictive model predicts the Sunflower recognition through trained data supervised technique with machine learning.

Tropical cyclones represent one of India's toughest natural threats. This paper lists the challenges and mitigation efforts to resolve significant disasters in the coastal areas of eastern India in particular. Humans must learn to counter and live with the effects of such natural disasters as they are unavoidable . The construction of natural hazard structures has always been supported by various national and international disaster mitigation organisations. New building design standards are proposed, as planning and growth to reduce the possibility of potential structural damage. Damage is caused to the roof, walls, openings, and foundation of a building due to the use of non-disaster proof construction practices. A brief introduction on the damaging effects of noteworthy cyclones is provided. It has been highlighted the increasing frequency of cyclones since the start of the 21st century due to climate change. A focus is maintained on the challenges faced by rural regions of India for the eastern coastline especially in the housing sector. Climate change and further cyclones have caused the loss of crops and thus livelihood, which is the main concern for the rural people. A section on the challenges to the housing reconstruction provisions for the rural community, as provided by the Government of India is depicted. Lastly, the paper provides various solutions for a cyclone-resistant structure and disaster prevention as per the challenges faced by the rural community.

A proposed method for automation of Indian vehicles number plate conversion is presented in this paper. Many vehicles are plying on Indian roads with varying in size and structure and hence there are a number of variations in number plates too. Although there are strict norms to be followed for number plate designs, however, it can be seen many deviations in actual number plates of many vehicles. It can be also observed that the number plates are written in many Indian languages, but the prescribed norm is Hindu-Arabic numerals with Latin letters which is prescribed by the licensing plate issuing authority. The number plate designs are varied according to types of vehicles, like, personal, commercial, public transport, etc. In order to design an automatic vehicle number plate, considerations of these factors are taken into account. The steps in the design include segmentation and character extraction from number plates and recognition of segmented characters in order to extract the complete information from number plates. The work in this paper is focused on Hindu-Arabic numerals with Latin letters. For feature extraction, Prewitt filter technique is used to extraction of characters from the vehicle number plates and connected component analysis is used for segmenting characters. Three different classifiers, namely, k-Nearest Neighbor, Artificial Neural Network and Decision Tree, are used in the experiments. Recognition accuracy up to 98.10% is achieved and is a promising factor for future research in this direction.

Sentiment Analysis (SA) is the area of research to find useful information using the sentiments of people shared on social networking platforms like Twitter, Facebook, etc. Such kinds of analysis are useful to make classification of sentiments as positive, negative, or neutral. The process of classification of sentiments can be done with the help of a traditional lexicon-based approach or machine learning techniques-based approach. In this research paper, we are presenting a comparative analysis of popular machine learning-based classifiers. We have made experimentations using the tweet datasets related to the COVID-19 epidemic. We have used seven machine learning-based classifiers. These classifiers are applied to more than 72,000 tweets related to COVID-19. We have performed experimentations using three modes i.e. Unigram, Bigram, and Trigram. As per the results, Linear SVC, Perceptron, Passive Aggressive Classifier, and Logistic Regression able to achieve more than 98% maximum accuracy score in classification (unigram, bigram, trigram) and are very close to each other in terms of performance. The average accuracy achieved by Linear SVC, Perceptron, Passive Aggressive Classifier, and Logistic Regression are 0.981573613, 0.976506357, 0.981573613, and 0.976690621. Ada Boost Classifier performs worst among all other classifiers with 0.731435416 average accuracies. The details regarding data collection, experimentations, and results are presented in the research paper.

In today’s era, there is a huge rise in demand for the data and information available in a printed medium. To resume time, cost, and energy, we have been developing numerous technology. One of them, convert JPG, JPEG, or PDF softcopy into text, this process calls Text extraction. Identification of characters and detection of words and texts is again an upfront challenging task. This paper is focused on an investigation of feature and text extraction from images using image recognition in Android. In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.

E-learning has become very popular and plays a positive impact on learning. Organizations have to consider many factors to make the learning process effective. The Virtual Learning Environment (VLE) provided by the Open University has several factors which affect the students’ performance. If these are identified correctly, better results can be obtained. Efficient machine learning models are built which predicts the final result. This paper applies many classification methods at first stage, and then apply Apriori algorithm in mining fuzzy association rules for a prediction system of learning. The system mines its rules through Open University, as there are about 170,000 students who are a part of several modules. The rules will be used to build a rule-based classifier to predict the final result incidence for the next course. This paper aims to compare the performance of e-learning using traditional classification and association rule methods before and after combination with fuzzy logic. The results showed that the use of fuzzy-Apriori algorithm provides a significant increase in accuracy compared to when not using any algorithm.

Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.

Prediction of the facts about any topic always sounds fascinating. It becomes more important and useful when it can predict the facts about healthcare information. In computer science, machine learning techniques/models can be used to predict important health care facts, further the study of the sentiments related to health care information can also play a significant role in the generation of important information. There are many international and national organizations like United States Centers for Disease Control and Prevention (CDC), World Health Organization (WHO), etc. that publish important healthcare data. These data sets can be used to predict important facts about the disease. Further, the datasets related to sentiments of the people like tweets on Twitter, posts on Facebook, and blogs are useful to study the sentiments of the people on a particular topic. Sentiments related to health care information, diseases, and epidemics are very useful. The study of these sentiments can help make better prediction systems and to generate fruitful health care facts. Further, the real datasets related to infectious diseases like COVID-19 can be used for regression analysis for making predictions regarding the upcoming cases. In this paper, we have presented a survey on the application of different machine learning techniques, that are used in the classification and regression analysis related to health care predictions and the study of sentiments. We have presented a review of major research studies related to healthcare between the years 2010 to 2020.

According to the latest news, the 3rd Generation cooperation Project has been taken the inventiveness of creating an interworking architecture involving cellular and WLAN as an additional feature to the cellular network requirements of 3GPP. This article provides an overview of an interworking infrastructure that allows mobile operators to provide the public WLAN dependent entrance service to various 3GPP network subscribers. The enabling features include 3GPP membership reuse, network availability, authentications relying on 3GPP systems, security keys agreement, USIM /SIM card authorization, user data service access and routing, as well as end-user paying. The function of interworking is accomplished without setting any particular 3GPP specifications for the new WLAN entrance networks, but instead by depend on existing features available in the traditional WLAN entrance networks centered on the IEEE 802.11 principles. In this paper, authors have focused on two key aspects of MTC devices, as currently discussed in 3GPP-Release 10. These are named as “the control of subscriptions” and the “congestion/overload control of the network”. A new solution is proposed for the latter one based on the handling of the bulk signals.

In the recent past, the cloud materials computing plays a vital role for keeping higher range of information in state of art of cloud environment. The information has to be kept in cloud may have large size that occupies more memory space in the cloud environment. The service provider have limitations in getting cloud space and providing space to the users to store more data limited and available space of cloud. We have proposed in this paper, an accurate and efficient video/image materials compression algorithm to minimize the data size to store in the cloud with less memory space. The novel algorithm for compression of image materials using block compressive sensing double density wavelet transform (BCS-DDWT) algorithm is proposed to compress materials. Compression of the image materials could be prepared based on various wavelets at variouspositions of decomposition. The concert investigation of BCS-DDWT with wavelets at 4 various positions of decomposition using BCS-DDWTcould be shown in the results. The performance of BCS-DDWTcompression is balanced by calculating parameters such as peak signal to noise ratio, mean square error, and bits per pixeland compression ratio.

In the growing technology automobile industries plays an importance role. The tire industry is a part of automobile industry. The growing practice in the tire industry is to replace Initial wheel size machinery of a car of a greater wheel dia. and a smaller aspect tyre with the similar diameter is replaced by the pneumatics. This activity is usually synonymous with retailing, and automotive manufactures are facing an increasing inclination for new vehicles to suit the bigger tyres. This paper explores general practise and its influence on the performance characteristics of the automobile. This research is aimed at demonstrating how the plus-size pneumatic affects all vehicle performance as well as stability. Any complex tyre influence that could impact the performance of the system will be addressed and preliminary performance data will be provided on the dynamic influence of plus sizing.

Cybercrime is a daily threat against organizations and partners of all sizes and with the rapid development of technology and the great dependence on it in some transactions, a type of serious threats has emerged that pose a high degree of risk to companies and organizations that rely on financial transactions in their work. This type of threat is called Business Email Compromise (BEC), which is a type of email phishing for financial purposes. This attack increased dramatically and caused very high financial losses to companies, especially in the period of remote work and in the Corona crisis, as it increased in the third quarter of the century of this year by 94%. This type of threat does not require a high percentage of knowledge, experience, or skills in deception and fraud, it requires only a reasonable level of social engineering. In this paper, we contribute to analyze this threat is, how it occurs, ways to avoid it or reduce its incidence, and impact on the organization.

Diagnosing a disease consumes a part of time, needs high technical methods but nowadays the smart technologies have been grown rapidly in the field of healthcare industries and also it improves the routine life of the patients, reduces the amount of work, treatment cost in the health care organization. Diseases prediction is one of the major challenges faced by society nowadays. The recent survey also stated that the death rate is remarkably high in CAD because most of the people are affected by cardiovascular diseases. Prediction and diagnosis of cardiovascular diseases is very essential nowadays to reduce the death rate and diagnosing it preliminary stage itself. In earlier studies, they worked with machine learning techniques to predict the diseases, but they are not given proper attention to identifying the feature with the help of proper feature selection methods. This paper proposed a new-found feature selection technique HRFLC (RANDOM FOREST + ADABOOST + PEARSON COEFFICIENT). This method helps to predict the diseases in a very efficient manner, and it improves the accuracy level in prediction.

Indian air travel and advanced technological development in the tourism society in India lead to the growth of Indian Hotel Industry. The Indian Industry staged a good progress due to the emergence of business hotel groups such as Taj Group of hotels, ITC Group of hotels, Ashok group. The growth and development of Indian hotel Industry is growth and development in India taken place with the growth and development of Human Resources Management and their practices in the Indian hotel which pave the way for their better performance. In this context, an attempt is made in this research paper to overview and analyse the organisational commitment of employees working in Indian Hotel Industry.

Stock price forecasting is a type of time series problem that forecasts the future price or status of a company on the basis of analysis of time respective values. As the price of stock or company varies with respect to time, its behavior can be analyzed by different machine learning approaches. In this work, methodology is proposed to evaluate the stock position with variation in time using deep learning approach such as recurrent neural network (RNN). This methodology used the technical parameters to evaluate the long term and short-term analysis of any stock or share. This approach also evaluates and gives suggestions to investors either to buy or sell any stock for long term and gives return at very low risk. In this paper work, hybridization of co-relation analysis and deep learning approach for stock price and long-term behavior analysis. The proposed work is termed as time lagged weight optimized RNN (TL-WO-RNN) that is adopted in this work and effectively predict the technical parameters and on the basis of that stock behavior is also predicted. The result analysis was performed on data from different sectors and such as Telecom, Powers, Manufacturing, Finance, Software sectors, etc. The result analysis shows the effectiveness of the TL-WO-RNN algorithm as compared to existing work.

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

Secure and reliable identity is essential for identifying a user accurately and providing services. The traditional centralized identity systems have several security weaknesses and do not support user control. Self-sovereign identity (SSI) has been proposed to provide user control and secure identity model. User record privacy and security is a critical factor for any healthcare information system. Healthcare is also a domain where SSI solutions can counter patient information privacy and security threats. This paper reviews the applicability of blockchain-based SSI solution in healthcare, their advantages and requirements. It further provides a model use case for demonstrating the SSI application in healthcare.

The agriculture industry is one that is highly resource- and labour-intensive. As such, farmers are increasingly turning to technology and automation to address this issue. However, agricultural robots are far too complicated, slow, and costly to be made publicly available. As a result, the agriculture sector still lags behind in integrating modern technologies. This research paper details the development of a low-cost agricultural robot for spraying fertilizers and pesticides in agriculture fields as well as for general crop monitoring. The prototype system is a two-wheeled robot that consists of a mobile base, a spraying mechanism, a wireless controller for controlling the robot movement, and a camera for crop health and growth monitoring as well as detecting the presence of pests in the agriculture field. Tests conducted on the prototype system show that while the productivity of the robot in terms of crop coverage is slightly lower than a human worker, the labour cost savings afforded by the agricultural robot prototype is much greater as it functions completely in an autonomous mode and only requires the operator to control the robot when placing it at the start of the crop path. Furthermore, the prototype system also provides greater resource savings and reduction in the contamination of underground water sources due to leeching process, thus achieving precision agriculture goals. Lastly, the excellent battery life of the prototype system ensures that there will be no increase in the operation times and reduction in the efficiency of the fertilizer and pesticide spraying process due to the recharging times when replacing human workers. Future recommendations include making the agricultural robot fully autonomous, using either a rail- or line-following system, to further reduce the labour requirements and costs.

Agricultural drones are Unmanned Aerial Vehicles (UAVs) applied to farming in order to help increase crop production and improve farm efficiency. Crop-dusting drones are able to spray powdered fertilizer, pesticide, or insecticide on crops from the air, resulting in reduced costs and increased efficiency compared to traditional methods. The objective of this research work was to improve and enhance the performance of the crop-dusting process using agricultural drones. This research paper details the development of an improved storage container design and stabilized release mechanism for crop-dusting. To stabilise the release of payload when the drone is moving or swaying in the air, a gimbal mechanism was added to the release mechanism in which weights were used to produce constant downward force which balances the mechanism. Tests conducted on the prototype system showed that it performs as required. The stabilization tests show an improvement in accuracy of granule spread when using the stabilized release mechanism developed in this research work. As such, the wastage of fertilizers and pesticides can be reduced. The prototype system was also found to much more capable at carrying heavier loads for a longer period of time compared to current designs. Thus, using the prototype system developed for this research work, the size of storage container as well as the weight of fertilizer or pesticide granule payloads can be increased.

The idea of the Internet of Things that everything is linked to anything and everything interacts with the other is a great idea, and that the topic of integrating these devices will add a remarkable change in this world and increase its prosperity, and this interconnection occurs through the exchange of data and information and facilitating our communication with the things around us. But questions about security and privacy remain. When this massive amount of data is exchanged, it will certainly be an attractive environment for attackers and hackers. In this paper, we will present the issues of security and privacy, their main requirements, potential risks and tools to defend these risks, and a detailed review of security attacks in order to be able to deter these attacks achieve confidentiality, security and reliability for users.

In recent era, IoT-assisted robotics (IoRT) plays a major part in collaborating IoT sensor nodes, robots and human beings to establish a collaborative communication. In this paper, we develop an IoRT system with a modern architecture that encompasses the integration of IoT sensor nodes and robots in smart cities. The study develops a cloud platform to access smart vehicle mobility data in smart cities without collision. A framework is then developed to assist the robots and IoT sensor node in managing and making quick decisions on traffic collision. Hence, it is developed to connect, share, and exchange the information between the cloud and the IoT nodes/robots. The experimental validation shows improved traffic control and collision free traffic in smart cities than other smart city traffic management mechanism.

UVM is the most important mechanism in the field of SoC Verification. But complex characteristics, implementation difficulties in UVM still persist which leads to at most challenging part for the improvisation. To solve this issue, a new verification methodology called VEDA (Verification Environment for IP Architectures) which integrates python as the verification language is proposed. The powerful features of Python are used to bring the most versatile environment for verification and to make the UVM as the user friendly mechanism. The paper proposes the verification mechanism for the different IP’s which plays asignificant role in all IoT applications. Further the proposed method is being compared to existing UVM and also other existing python HDL tools and proved to be more advantages when compared to the existing methodologies.

In digital circuits, adder is one of the most widely used circuit and it is one of the main components in every Arithmetic and Logic Units (ALU) of microprocessors and microcontrollers. Adders are used to perform arithmetic and logical operations such as Addition, Multiplication of numbers and many other operations. The performance of the Adders will affect the performance of ALU and in turn microprocessor and microcontrollers and the application which use them. Hence the design of adders is important in digital design. The analysis by considering the Power consumption and delays of Full Adders implementations in 180 nm, 90 nm and 45 nm technologies is discussed in this paper.

Classification of wheat after harvest is a challenging task. Several techniques are prevalent for measuring the geometrical parameters of the wheat and for scrutinizing the wheat seeds. Parameters used for the classifier includes area, perimeter, compactness, length of kernel, width of kernel, asymmetry coefficient, length of kernel groove. Neural network pattern recognizer is used for the classification of the wheat seeds in this paper. The database for the Wheat seed is downloaded from the UCI machine learning repository. Confusion matrix and ROC is computed as the performance measure. Results show that neural network pattern recognizer is an apt tool for classification of wheat seeds with an overall accuracy of 96.7%.

Recently, extraction of text or characters from images has become a wide field of research in the field of image analysis, neural networks, artificial intelligence and machine vision. The aim of character recognition is to extract and recognize the text from images and can be thus used to analyze contents, documents, or retrieve certain information etc. Image analysis has various processes and algorithms available for text extraction like edge detection, point detection, corner detection etc. for extracting the text from images. Many steps are involved in text recognition including pre-processing, segmentation, feature extraction, and classification. This paper addresses FAST algorithm for corner detection applied on the images for text recognition. The FAST algorithm detects and determines the presence of a corner by testing a small area around the potential center of the corner with the help of intensity value. This approach of text recognition can be efficiently used even with images with noise content or blurs.

In Mobile Ad Hoc Networks (MANET), during routing, there may be possibility of route recovery delay and overhead. Also there may be issues due to reduced lifetime of link and node. In order to overcome these issues, in this paper, an optimization technique for fault recovery and fast data transmission in MANET is proposed. In order to avoid the faults due to frequent disconnections and node failures, the path with least mobility and high residual energy is selected for transmission. During data transmission, if majority of the neighbours are changed or if the residual energy is low, then the source will choose any shorter alternate route by applying the optimized recovery mechanism. By simulation results, we show that the proposed technique enhances packet delivery ratio and throughput and minimizes end-to-end delay, energy consumption and control overhead.

These benefits of the Usages-Based Insurances for the car that covers the overall traditional ratings techniques are also have been examined for writing more than the four decades. Despite these adoption in the insurance the markets act as a moderate. This paper looks to set up the practicality of presenting armada the Telematics based Insurances by the investigating the view of the insurance administrators, following specialist organizations and corporate armada proprietors. At its center, the investigation includes a kind of SWOT-examination to assess the Telematics based Insurances against customary premium rating systems. 25 keys partners in the Malta, a nation with an insurance industry that speaks to others in microcosm, were met to build up our investigation. Everyone affirm so that neighborhood back up plans have interests in such insurance conspires as upgraded armada the board and observing convert into an improved insurance hazard. The discoveries introduced here have suggestions for all partners as this is contend for telematics upgradation of armada board, TBI improves risk administration for back up plans and appropriation of this innovation is subject to telematics suppliers expanding the apparent control by safety net providers over dealing with this innovation.

The emergence of Web 2.0 tools has changed the consumer habit of using the Web. It can be used for promoting brands and products. This paper investigates the uses and effect of promotional marketing in the Web 2.0 tools on the purchase decision. A structured questionnaire was used to collect fresh-hand information from the Gen Z category. The study results reported that Web 2.0 tools provide more useful information to consumers. The fun and humorous aspects highly attracted the Gen Z category. Hence, they trusted information shared via Internet and social networks. Moreover, Web 2.0 tools highly influence the purchase decision of Gen Z. This study findings will indicate how fast the promotions in the Web 2.0 influence the purchase decision of the young customers.

Two dimensional (2D) Photonic crystals (PhCs) based cross waveguide structure (CWs) consisting of resonant cavity is investigated in this paper. Investigations are done using Two Dimensional Finite Difference Time Domain (2DFDTD) method. Transmission characteristics have been obtained for different radii of central rod placed at crossing point of two waveguides and number of regular rods surrounding central rod. Nonlinear behavior is also studied for different operating wavelengths.

Air suspension system is been used by vehicle manufacturers since decades. Earlier it was only used by the commercial vehicle but soon the manufacturers and the users found the need of air suspension in commercial vehicles as well. Nowadays this system can mostly be seen in luxury vehicles, the application of air suspension has come a long way, earlier when it was installed in commercial vehicles, it was generally used for raising and lowering ride height to accommodate different sizes of trails, but later air suspension system majorly started to be used as a luxury feature because manufacturers invented that it was providing supreme ride and comfort especially when comparing with traditional spring mechanism used in suspension. Therefore all the luxury cars are using air suspension to provide supreme comfort during riding even through rough terrains. This paper discusses about the different applications of air suspension discussing about the working and its advantages and disadvantages.

Glucose detection has been an area of immense interest of scientists, researchers as well as clinicians worldwide for quite a long time. There have been many methods developed by the researchers to detect blood glucose but the detection methods were mostly limited to the order of ~0.1 mM. In this paper, we propose a very simple and cost-effective method to detect nanomolar (nM) levels of glucose in the human blood serum phantom sample. This method reports a significant improvement over the earlier known methods and can be used in extremely low-cost settings by scientists and researchers working towards the development of various chemical and biological sensors.

In this paper, recent results regarding rare earth element doped with chalcogenide glasses for optical gain media such as amplifiers and lasers are reviewed. Rare earth Chalcogenide glasses are formed when chalcogen elements (S, Se, Te etc.) are mixed with new additive elements (Ge, Ga, Sb, As etc.) and also with rare earth elements (Er, Dy, Pd, Pr etc.) from periodic table and prepared by melt quench technique (MQT). Rare earth element doped with chalcogenide glasses has been emerged as a suitable candidate for applications in the numeral passive and active. Among various materials, chalcogenide glasses show significant improvement in their emission properties in the optical communication range and spectroscopic properties. Finally, rare earth doped chalcogenide and optical fibers show potential for high power mid-infrared gain medium and their spectroscopic properties as well.

Value stream mapping (VSM) is a vital lean manufacturing tool accustomed to identify and reduce the process lead time and the work in process inventory for improvement of the overall efficiency of an organization. This paper aims to investigate and implement value stream mapping in water distillation plant. The data is collected from the various production processes carried out in plant after analyzing the all collected data. We had prepared the current state map and value-added activities and non-value-added activities had found. After conducting an impressive investigation on non-value-added activities, suggestions are made to minimize the process lead time and work in process inventory. The lean techniques like Kaizen, 5S are used to make necessary changes to improve the overall efficiency and after considering the proposed changes future state value stream map was drawn. After successful implementation of VSM Technique in water distillation plant the improvements are, the processing time is reduced from 171 s to 143 s and lead time is decreased from 8057 s to 6344 s.