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Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

Due to the non-linear and highly volatile nature of the Stock market, it has become a very challenging task for researchers to make accurate predictions. Improving the efficiency of predictions has become the main goal of many researchers. From the traditional approach of working with historical dossiers to using the latest machine learning and deep learning techniques, researchers are busy finding out the best possible ways of accurate predictions. Many new models are suggested that can make good estimations of stock prices. Investors are interested in knowing both the immediate next-day prices and as well as future share prices in the long run. This paper inspects the algorithms and techniques that are useful for making accurate predictions.

The 3D printed parts made by using the additive manufacturing (AM) process are presently high in demand due to many advantages. This includes well-known features such as the cost-effective method of producing customized parts made of thermoplastic material and having a shorter lead time for a wider range of applications. However, it has also a few limitations like poor dimensional accuracy and resolution. Thus, this technology is not suitable for parts having interior details. So, it is very much essential that the parts being produced using this technology must be characterized and analyzed accordingly so as to increase their potential use in the present market. For this, a review paper has been made in order to study all the main characteristics affecting the rise in demand of parts. Several statistical and optimization techniques have been examined and reviewed. The optimal parameters corresponding to the specific characteristics causing an effect to a large extent have been observed.

Since food is one of the essential human needs, studies on this topic have always been a global concern. With the advent of COVID-19 and the emergence of many problems in all aspects of the food supply chain (such as production, transportation, distribution), this issue has become doubly important. This paper discusses an MINLP optimization model for handling the impact of the COVID-19 pandemic based on the food supply network through Food Hubs (FHs). In this research, the concept of FH has been used for a more effective and faster connection of consumers to production sites. Due to prevention of the spread of coronavirus and the quarantine conditions, the areas have been divided into two parts (high-risk and low-risk) and two scenarios have been defined for this supply chain. The purpose of this paper is to reduce costs and environmental impacts as much as possible. The proposed model is solved by GAMS software for small- and middle-size test problems, and it is solved with genetic optimization algorithm as a meta-heuristic approach for large-size problems. Also, to solve the developed linear multi-objective model, augmented ɛ-constraint approach is applied, and a real case study from Iran is examined to illustrate the validation of the proposed model. Numerical and computational results are provided to prove the efficiency and feasibility of the presented model. Finally, sensitivity analysis is presented to evaluate the effect of changing some parameters on variables and objective functions.

The solar panels installed on a CubeSat are considered the main energy source of a nanosatellites. The deployment mechanism of a solar panel must be analyzed and tested extensively. Any suggested solar panel design should present a low vibrating free spinning deployment mechanism. This paper examines various types of solar panels to reach a conclusion of the efficient design when deployed on a 1U or 2U unit. However, calculations, analysis, simulations do not always give an extensive picture of how the satellite shall behave during deployment. Thus, testing in a microgravity environment gives a more accurate answer of how the satellite shall behave. In our work, various solar panels mechanisms are developed and eventually tested in microgravity. The first accordion structure for a 1U structure is tested in a microgravity environment through a parabolic flight with the National Research Council Falcon 20 aircraft. The results are recorded and analyzed to optimize the next design. The second design is based on a drag-sail mechanism for a 2U structure. The design is improved upon the first experiment results for the next parabolic flight. The simulated amount of power generated in orbit is also a main factor in our evaluation.

The given paper is focused on experimental study of one particular category of DoS attack (Denial of Service) in cloud computing network known as TCP SYN flood attack, and its effect on the resource availability and the cloud service factors. The attack typically takes up the general services of the cloud and the resources of the cloud, thus denying of proper services to the genuine users. The resources of the cloud server are drained and hence any other incoming requests would not be responded, thereby denying the access to the legitimate users. The assumption here is that the data being transmitted between client, and server is a multimedia data. In this paper, we have tried an experimental study on TCP SYN Flood attack and tried to see out the various parameters affected by the attack.

Mobile ad hoc networks (MANETs) are self-organized, self-configuring collection of autonomous mobile nodes that temporarily forms network and does not require any infrastructure or centralized administration for data communication. The network formed by MANET is vulnerable to threats from malicious nodes which can lead to disruption of the network. The routing protocols available for MANETs also do not provide any mechanism to counter threats or identify malicious nodes or behaviour in the network. In this context, this paper is aimed to find and evaluate the performance of routing protocols when exposed to threats. This paper considers rushing attack and presents a detailed study on their impact in MANET routing. A simulation-based performance analysis on one reactive routing protocols viz ad hoc on-demand routing protocol (AODV) has been performed, and a defence mechanism based on processing delay has been devised to prevent rushing attack. The paper contains various sections like Sect. 1 contains introduction followed by few existing approaches for countering rushing attack in Sect. 2. Section 3 provides the delay-based approach for preventing rushing attack. Sections 4 and 5 present the algorithm for countering rushing attack and simulation environment, respectively. Results and discussion are given in Sect. 6 followed by comparison with other model in Sect. 7. The paper ends with the detailed conclusion given in Sect. 8.

In recent years, Weibo and news commentary information on the Internet have shown a trend of massive growth. It has become very difficult for people to accurately analyze public opinion from massive data. In this paper, a new classification method of meteorological public opinion based on deep random forest is proposed, aiming at the difficulties of public opinion analysis and the scarcity of data features in Weibo meteorological domain data. The method is used to analyses the evaluation data of Weibo on the basis of the meteorological data of Sina Weibo, thus judge the tendency of network meteorological public opinion comments when natural disasters occur. Firstly, we acquired and trained meteorological commentary data. Then used the method of word2vec to vectorize comment information. At last, the vectors were classified by the deep random forest algorithm, meanwhile, continuously optimized the classifier until the training set size reaches the set threshold. The experimental results show that the method proposed in this paper can better judge the public opinion tendency of network meteorological comments and has achieved better results compared with traditional classification methods.

This paper aims to improve the performance and positioning accuracy of a robot by using the particle filter method. The laser range information is a wireless navigation system mainly used to measure, position, and control autonomous robots. Its localization is more flexible to control than wired guidance systems. However, the navigation through the laser range finder occurs with a large positioning error while it moves or turns fast. For solving this problem, the paper proposes a method to improve the positioning accuracy of a robot in an indoor environment by using a particle filter with robust characteristics in a nonlinear or non-Gaussian system. In this experiment, a robot is equipped with a laser range finder, two encoders, and a gyro for navigation to verify the positioning accuracy and performance. The positioning accuracy and performance could improve by approximately 85.5% in this proposed method.

In any corporation, if a significant number of employees leave their job with a short notice period, it may lead to a reduction in overall throughput which in turn will certainly have an impact on the turnover. Companies need to spend additional efforts in terms of time and cost to fill up the vacant position without any substantial loss to the ongoing business. To avoid these situations, we can use machine learning techniques to predict employees who are planning to leave the company with the help of some related data. One more way is to identify the features which inspire employees to leave their job. Refining such features in the company also will result in reducing the employee attrition rate of the company. In this paper, we attempted to predict employee attrition rate using the classification algorithms, namely Decision tree, Random forest, K-Nearest Neighbourhood, Neural Networks, extreme gradient boosting and Ada-Boosting. We also have applied regularization for every algorithm to find the precise parameters to predict the employee’s attrition rate considering the HR-data set from the Kaggle website which consists of 35 features including 34 independent and one dependent feature which is our attrition feature with Yes/No values in it. In this paper, we are going through different steps to finally obtain an accuracy of 88% with good precision and recall values.

In computer vision systems, however, retrieving a necessary image for a normal user is difficult. Over the last two decades, several studies have been conducted to improve the efficiency of automated image annotation, which has mainly concentrated on content-based image retrieval (CBIR) and attempts to identify specific images from a large dataset that are close to a query image. Various hybrid feature descriptors based on image cues such color, texture, shape, and so on that depict images were examined. For more than a decade, machine learning has been gaining traction as a viable alternative to hand-designed feature engineering. Feature extraction techniques and mathematical models are used to improve the performance and complexity of the image retrieval process. Furthermore, using the existing AlexNet convolutional neural network classifier, to increase the accuracy and performance of retrieval of Corel and Wang Datasets. Present state-of-the-art methods are described from different viewpoints for a deeper understanding of the development. Over the last period, the survey paper has given a comprehensive overview of hybrid feature extraction methods and machine learning-based enhancements for CBIR. In this paper, we hope to include a succinct overview of recent developments in CBIR and hybrid feature extraction methods, as well as machine learning-based enhancements for content-based image retrieval.

Machine learning has rapidly gained traction in a variety of fields, including science, healthcare, engineering, and biotechnology, in recent years owing to its effective functioning mechanism. Health care has always been a key priority for any government as the industry has made significant progress by using machine learning, artificial intelligence, and deep learning for disease prediction and diagnosis. The central aspect of this paper is to evaluate different machine learning algorithms and classification techniques in order to detect and predict different chronic diseases. The paper discusses supervised classification strategies for detecting diseases such as cancer, psychological disorders, and cardiac disorders, as well as various bioinformatics and biomedical research challenges. The comparison between classification techniques like support vector machines, logistic regression, decision trees, random forest, and Naive Bayes classifiers has been observed in numerous diseases. The algorithms that are specifically applied in the medical applications and in healthcare sector enabled the clinical experts and physicians to watchdog, diagnose, and monitor the disease effectively and perform appropriate measures in the shortest possible duration. Decision support systems benefit the physicians for effective and timely decision-making capabilities in case of chronic diseases. The paper reviews the implementation of machine learning and deep learning which has undoubtedly contributed toward health informatics, healthcare systems including bioinformatics. After discussing the techniques and comparison of numerous classification algorithms in several diseases, the ones which have efficiently produced an appropriate result in an early detection of diseases have been highlighted. In order to emphasize the issues that must be considered while implementing the methodologies and classification algorithms for an early illness detection system, several future directions are described.

Recently, small-scale grid-connected PV systems are acquiring familiarity in institutions and industries mainly due to their clean and climate-friendly attribute. A 104 kW grid-connected PV system at Kattankulathur, Tamil Nadu, is discussed in this paper. A software tool is essential to estimate and predict solar energy power generation for a particular location. This paper mainly deliberates the design aspects using a suitable software tool for the grid-connected PV system. The best approach for designing and evaluating energy production in PV systems is PVsyst software. Therefore, this paper demonstrates the step-by-step procedure followed for creating the 104 kW PV system. The approach towards loss distribution and performance ratio analysis is also presented. This paper is proposed as a guide for the researchers working in this field of designing a PV system based on the load requirement.

Stress is a mental condition that affects every aspect of life leading to sleep deprivation and various other diseases. Thus, it's necessary to analyse one's vitals to stay updated about one's mental health. This paper presents an effective method for detecting cognitive stress levels using data from a physical activity tracker device. The main goal of this system is to use sensor technology to detect stress using a machine learning approach. Individually, the impact of each stressor is assessed using ML models, followed by the construction of a NN model and assessment using ordinal logistic regression models such as logit, probit and complementary log-log. The paper uses heartbeat rate as one of the features to recognise stress and the Internet of Things (IoT) and Machine Learning (ML) to alert the situation when the person is in real danger. The patient's condition is predicted using machine learning and the patient's acute stress condition is relayed using IoT. Based on the heartbeat, a prediction of whether a person is under stress or not can be made. The paper presents a model that can predict stress levels based entirely on electrocardiogram (ECG) data, which can be measured with consumer-grade heart monitors The ECG's spectral power components, as well as time and frequency domain features of heart rate variability, are included in the model. The stress detector system takes the real-time data from the IoT device (sensor), then applies a machine learning model on the data to detect stress levels in an individual and eventually informs/alerts the individual about their stress condition. By collecting data, this system is tested and evaluated in a real-time environment with different machine learning models. Finally, a comprehensive comparative analysis has been depicted amongst the applied models with Random Forest Classifier showing the highest accuracy. The novelty of this work lies in the fact that a stress detection framework should be as unobtrusive to the user as possible.

Diabetes is the world’s fastest-growing illness, causing a slew of complications. One of these conditions is diabetic retinopathy (DR) which causes retinal lesions affecting the vision. There are several levels of DR, ranging from moderate to extreme. Early detection and treatment of DR can decrease the risk of loss of vision. Presently, DR detection is a laborious and blue-collar process that needs qualified ophthalmologists to inspect digital color retinal fundus photographs. As a result, various machine vision-based methods for automatically diagnosing diseases are explored in the literature. Deep learning has emerged in recent years achieving better performance than existing methods in many areas, especially in analyzing and examining the medical images. Deep CNNs are broadly used method in analysis of medical images. To highlight the role of CNNs in DR detection and classification, various recent papers have been studied and considered. The characteristics of various available DR Datasets of colored fundus images are also discussed. The paper suggests a set of steps that will form a process such that it will be promising at all the stages of DR detection/ classification. The explanation behind the suggested set is derived by comparing the results of the papers. Since the results of any method depend on the input, therefore, to see the effects of selected preprocessing method, i.e., CLAHE, was applied to images from most widely used dataset: Kaggle dataset. The processed images were noise-free also the lesions were clearly visible.

In the recent years, the construction industry is focussing to develop a steel and composite structures than the concrete buildings owing to the advantage of ease and speedy construction, effective utilization of materials, lightweight and strength, etc. In modern construction, the space frames were used for industrial structures without intermediate columns and for aesthetic appearance. In practical, the space frame structures were normally used for roof covering than the floor slab. The space truss having concrete slab above the space grid with proper connection mechanism between the grid and concrete will be used as deck floor for occupants in the multistorey building. This paper explains the studies done on the space truss structures both experimentally and analytically during previous years. The parameters which affect the strength and overall stability of the space truss structures were highlighted in this paper. The double-layer grid can be used as the floor slab; discussion was also made in this review paper.

Teaching and learning methodologies are constantly evolving, in order to contribute to the success of students’ learning. This process is intended to be increasingly practical and real, so the implementation of methodologies such as project-based learning (PBL) appears as important allies. This paper is particularly focused on the implementation of a PBL methodology in translation classes, presenting some previous cases that contributed with different perspectives on its application and development. Despite the contributions of these different approaches, this paper brings a different one focusing on the implementation of a case study supported by a PBL approach in the subject of Technical Translation at the Porto Accounting and Business School, a higher education institution in Portugal. The main goal was to provide a hands-on experience carried out in a digital environment by conducting and managing translation projects in all their stages. This methodology had a circular nature, allowing all students to experience the roles that the different actors in a translation project have and that will lead to their skills’ development. Using pre- and posttests, as well as learning portfolios, we wanted to see whether or not the students would identify any of the underlying principles of PBL as personal learning outcomes. The methodology is detailed as well as the assessment results of the implemented strategy.

According to survey in January 2020, over internet almost 1,295,973,827 web sites are hosted. Among them 72% websites are vulnerable to different attack like SQL Injection, Cross Site Scripting, Brute forcing attack, Phishing attack, password attack, birth day attack, malware attack and man in middle attack now days many hackers/attackers are trying to bypass the security mechanism by using new techniques so it’s challenging task to respond newly and unknown attack with an effective solution. In this paper i am trying to find out the techniques, tools, and solutions used to detect attacks, such as intrusion detection systems (IDS), Web Application Firewall (WAF), machine learning (ML) techniques, this tools and techniques is used to discuss analysis of traditional technologies, drawback and produce more effective solutions. In this paper we compared the different web application firewall correspond to its policy control and one proposed a new web application firewall. In the proposed web application firewall we have discussed three approaches.

Emotion detection using Electroencephalogram (EEG) is a reliable approach for the formulation of human brain signals as this does not depend on humans' posturing or pretentiousness. Analyzing every brain region generates noise, redundant information. This increase more complexities in calculations. Ultimately, handling unwanted and excessive data degrades the detection accuracy as it calls for use of additional and complex hardware. Therefore to overcome this challenge, this paper proposes a detailed study and understanding of different brain regions which contribute most to emotion generation and which are very much vital for emotion analysis. This work provides guidelines in neuroscience and establishes that a particular brain region is vital which provides sufficient information and EEG data for emotion recognition. In this paper features of differential entropy, Hjorth parameters, and power spectral density (PSD) are used to train bidirectional long short term memory (BiLSTM) network. The performance of the proposed technique is evaluated on the DEAP benchmark database for different brain regions namely the prefrontal region, frontal region, temporal region, parietal region, and occipital region. The results show that the frontal brain region has the highest emotion recognition rate compared to other regions and is vital for emotion recognition.

With the rapid advancements of various information technologies in recent years, many new forms of e-learning schemes have emerged. Due to the various issues with such e-learning schemes, however, the commonly adopted teaching method is still the traditional face-to-face classroom in practice. In such a teaching mode, the three key elements of all teaching modes, i.e., theory, experiment, and practicing, are separate and independent of each other, which largely degrades the learning efficiency. To address such a practical issue, based on the objectives of professional personnel training for navigation and control, this paper presents a curriculum teaching model that integrates the three learning key elements together. Specifically, this paper shows the architecture of the curriculum construction mode first, and then presents the composition and function of the student dimension, teacher platform dimension, laboratory server dimension and laboratory equipment dimension in the navigation and control course group, respectively. Finally, with the course of “principle of inertial sensor” as a case study, this paper introduces the teaching process of using the teaching mode, which integrates the three key teaching elements together in the course. The practical teaching results show that this proposed teaching mode could not only make the teaching process of engineering courses intuitive and easy to understand, but also enable students to participate in the theorical, experimental, practical teaching and learning process throughout the presented mode. This mode could largely enhance both students’ learning enthusiasm and the efficiency.

In order to solve the practical problem that the traditional operational effectiveness evaluation method is difficult to effectively evaluate the new type of autonomous swarm operational effectiveness, this paper proposes an aviation swarm operational effectiveness evaluation method based on the practical needs of the future autonomous swarm operational effectiveness evaluation. Firstly, based on the hierarchical thinking, the operational effectiveness index system for aviation swarm was constructed. Secondly, using the exponential logarithm method, the evaluation model of aviation swarm operational effectiveness is established. Finally, the ground attack from aviation swarm is taken as an example to verify the proposed method. Simulation results show that the method proposed in this paper can measure the emergence of aviation swarm operation capabilities to a certain extent.

Aiming at the problem that BOC (Binary Offset Carrier) signal is difficult to be detected by traditional means under low SNR, this paper proposes a phased BOC signal detection algorithm based on cyclic stationary feature. Firstly, this paper introduces BOC signal model, proposes an improved cyclic stationary feature detection method, analyzes the difference of gray images under binary hypothesis, and utilizes this difference to extract features by putting the two types of images into the convolutional neural network, and then uses the trained network for detection. Furthermore, in order to detect signals more efficiently, energy detection is carried out at first. If no signal is detected, the improved cyclostationary detection is adopted in the second stage. Simulation results show that the detection performance of the proposed method is significantly better than that of the traditional cycliostationary detection and energy detection when SNR is less than −7db.

Based on mutual information, this paper proposes a method of aviation safety dimension reduction, which is used to construct the causation model of aviation accidents under uncertain environment. First, based on the Bow-tie model, a comprehensive m comprehensively considering many factors such as the cause of the accident, possible consequences, and control measures. Aiming at the problem of high-dimensional modeling of causal factors, a mutual information measure was proposed to measure the causal strength between causal factors and aviation safety, and screen out causal variables that have a greater impact on aviation safety. For mutual information needed to solve the problem of multidimensional variables cause of joint integration it is proposed based on sampling in conjunction with low deterministic kernel density estimation method for solving joint integration, convergence and high accuracy. Taking aviation safety as an example, the correctness and scientificity of the method in this paper are verified.

Aiming at the problem of civil aviation flight conflict, combining case-based reasoning and artificial neural network in artificial intelligence, this paper puts forward two kinds of flight conflict relief models, and compares them. In this paper, ADS-B data is quantized according to the requirements of supervised learning for data format. On this basis, a case base is established to provide conflict relief instructions for the target case by calculating the case matching degree. This paper uses the processed data to train the artificial neural network, and uses the validation group data to verify the performance of the artificial neural network. By analyzing the fusion matrix and ROC curve, the classification accuracy and the sensitivity of the neural network are obtained. The results show that the two methods can give the release command of flight conflict, and the accuracy of artificial neural network is higher than that of case-based reasoning.

Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.

This paper surveys several recent abstract summarization methods: T5, Pegasus, and ProphetNet. We implement the systems in two languages: English and Indonesian languages. We investigate the impact of pre-training models (one T5, three Pegasuses, three ProphetNets) on several Wikipedia datasets in English and Indonesian language and compare the results to the Wikipedia systems’ summaries. The T5-Large, the Pegasus-XSum, and the ProphetNet-CNNDM provide the best summarization. The most significant factors that influence ROUGE performance are coverage, density, and compression. The higher the scores, the better the summary. Other factors that influence the ROUGE scores are the pre-training goal, the dataset's characteristics, the dataset used for testing the pre-trained model, and the cross-lingual function. Several suggestions to improve this paper's limitation are: (1) assure that the dataset used for the pre-training model must be sufficiently large, contains adequate instances for handling cross-lingual purpose; (2) advanced process (fine-tuning) shall be reasonable. We recommend using the large dataset consisting of comprehensive coverage of topics from many languages before implementing advanced processes such as the train-infer-train procedure to the zero-shot translation in the training stage of the pre-training model.

Artificial Intelligence (AI) is a new field triggered in computer science in order to solve complex problems like cooperation, distribution, and communication. AI has many research areas for the development of complex systems. Multi-Agent Systems (MAS) are one of reach of using autonomous systems which have actors’ acts on their own based on user requirements to solve their problems. A new development of programming language called Agent-Oriented Programming (AOP) for programming autonomous agents and multi-agent system MAS. The agent in MAS acts in an autonomous way without any human or system interaction. The agent has the ability of automated action in order to reach its goal. An agent has the Belief-Desire-Intension (BDI) architecture in order to define the behavior of the system using Agent-Oriented Programming. This paper shows a proposed model which is an intelligent multi-agent system for e-bookstore that communicates with each other using a Foundation of Intelligent Physical Agent (FIPA). Also this paper shows the search performance within the communication results worked on Amazon Dataset and evaluates the quality of intelligent agents of E-bookstore system.

The production of grains plays a vital role for the economy of any country. Smart methods are required that accurately helps to increase the efficiency of the grain production. The use the techniques of the tools of artificial intelligence, deep learning and image processing are highlighted in this paper. These procedures achieve especially compelling results for the revelation of diseases using the photos of leaves, seeds or reap field. In this special circumstance, the work presents a review that centers around precision agriculture for the high formation of quite possibly the main reap in the world: RICE. In this paper, the classifiers SVM, CNN and VGG16 which utilizes the methods of Artificial Intelligence utilized for crop illness location, seedlings wellbeing, and grain quality are reviewed.

During the challenging times of COVID 19, major shifts in the learning and interaction process have been observed especially in the field of education and platforms of wider interactions like seminars. Online medium has been emerging as the order of the day. In this context, this paper looks at a new challenge that is being faced by various institutions while carrying out the interview process or interactions online or on a telephone. The recruiter might miss certain points when evaluating a candidate or sometimes the finer details of an interview are lost in communication, the interviewer might also not be able to recall the details of a particular interviewee at a later point in time. These are some of the challenges that would ultimately reduce the effectiveness of an interview process. We are developing a platform that extracts all the essential information delivered by the person who is being interviewed from the data received by the candidate during the process. The paper makes use of Natural Language Processing (NLP) to extract the key information from an interaction by extracting certain features or important key points by making suitable algorithms with the help of language dependencies. The paper also dealt with the outcome of the interview in the sense that, whether the candidate got selected or not based on her/his answers and how similar they are with the data provided by the company which clearly puts forward what they are looking for in an employee.

The huge collection of data under the domain of health informatics has always been of crucial importance in giving insights into human health and its sundry causes. With technology rising day after day, this data can be visualized under different lights which are depicted in the following paper. Data analysis is the answer to the challenges of the healthcare industry because of the plasticity offered in implementing its techniques in various frameworks and technologies. A notion about Machine learning and its association with big data has also been discussed here. The Machine learning techniques have always made analysis better; with a similar analogy, the paper gives a glimpse of ameliorating the patient’s lives who are looking for healthcare facilities in the Bengaluru region.

Internet of things (IoT) refers to a network where the devices included in that network are connected with each other through a common medium, in this case, the Internet, in order to share or exchange data with other devices in the network. The paper concentrates on Management of Security of these devices in the IoT network along with their maintenance, accessibility, etc. The main problems faced are data leakage, data alteration/modification, access to private data or important transactions, data loss, etc. This review paper refers to various ways to improve the existing IoT system with the use of different consensus algorithms and techniques. It also covers the security and data privacy of systems like smart homes and smart cities through modified blockchain systems.

With an increasing number of Indian manuscripts being digitized, the subject of their era prediction is readdressed to interpret the socio-economic fabric of different periods. This paper describes a novel approach to estimate the era of Indian manuscripts from their scanned images using convolutional neural networks (CNN). The method primarily uses image processing to harness visual features from small image patches and classifies them based on the difference in writing styles in terms of strokes and letter formation. We follow a two-step approach of language prediction followed by a separate era prediction model for each language to achieve optimal results. For this paper, we restrict consideration to six Indian language manuscripts written between the sixteenth and twentieth centuries. Conclusively, our model outperforms other well-known architectures and gave over 90% and 80% accuracy on the training and validation data, respectively.

Due to advancement of new edge medical technologies, many methods have been applied to solve medical issues including machine learning approach. Cardiac Arrhythmia is one of the common diseases which can be solved using various machine learning approaches. There are many approaches which have already been introduced to classify arrhythmia and abnormality detection. This paper has a solution, introduces supervised and unsupervised models in which supervised models generate a good classification result. However, in this paper, we have also introduced a deep neural network classifier and used for prediction of arrhythmia if present based on some predefined value. In this paper, we have also connected it to the user interface to which the native users can check the level of arrhythmia.

Medical and healthcare monitoring system has had rapid growth in not only hospitals but other health-related centers as well. Effect of a wrong prescription leads to death of a patient, also unnecessary medicine prescription is the burden for the guardians or caretakers. IoT (Internet of things) allows a variety of devices to interact with each other to enable monitoring, storage, analysis and display. This paper explores the developments taken in the field of patient monitoring systems to avoid wrong and unnecessary prescription, and through this research, we found that selecting IoT protocol to design the system is important and depends on the system requirements like number of data sending nodes (Data Producers), network traffic hitting the server, scalability, network latency and real-time requirement. This paper proposes the design of a real-time patient monitoring system with microservices architecture to send patient’s data from ICU to the doctors and guardians. The proposed system also helps to reduce unnecessary prescription and billing.

Resource allocation and power management have become challenging tasks in cloud computing. Since workloads change over time, dynamic cloud resource allocation and power management solutions are needed, that can intrinsically adapt to the demand of the resources. This paper demonstrates the use of deep reinforcement learning algorithms for the task dispatching and power management in the simulated Alibaba cluster environment with the goal of achieving better power and latency management than traditional task scheduling algorithms. In this paper, we propose a hierarchical RL model for resource allocation and power management which obtains better results in terms of latency as well as power usage, than traditional task scheduling.

The last few decades have seen tremendous growth in the IT industry where offshoring software has become a general phenomenon. Due to this offshoring, development and maintenance teams are generally located in different time zones along with at distinct locations. This separation of teams at different locations leads to the problem of sharing information in a consistent manner. In this respect, the Semantic Web plays an essential role as it helps in sharing and reusing already available information. Due to the emergence of the Semantic Web, it has been applied in various Software Engineering tasks and issues. Among those various issues, one is to provide a model or system, which can produce automatic documentation for every version of a software system for their better understanding and maintenance. Therefore, this research paper proposes an ontology driven software development approach for better understanding and maintenance of software system. The research approach consists of a multi-phased framework in a cyclic manner that generates the various versions of software and their associated documentation. To implement and demonstrate the research approach, we picked a case study. The results of the implementation demonstrate that the paper successfully generated all versions of the software along with its automatic documentation. Moreover, the documentation is accessible in machine-understandable together with human-understandable form, which leads to the generation of consistent information without any ambiguity. Thus, the research work helps in better understanding and maintenance of software systems.

These days signal processing has the great importance in extracting important pathological attributes of the subject (patient). This paper covers important aspects of Electrocardiogram (ECG) signal analysis by proposing emerging tool. The degree of morphological beat-to-beat variability has been examined using a spectrogram technique on real-time ECG datasets. It provides time varying spectral density description of the ECG signal. Out of 49,181 total beats, the proposed technique presents duplicity (D) of 0.4% and detection rate (DR) of 99.48%. Some of the possible future directions, that the research work carried out in this paper can take, are also outlined in conclusion section.

Due to involvement of different types of noise, electrocardiogram signal needs robust techniques for its analysis. For that purpose, the theory of chaos analysis is applied as a feature extraction tool on different pathological datasets obtained from different cardiology laboratories. This paper presents the important observations on attractor plots obtained at different time delays. It facilitates the cardiologist in segregating the normal and abnormal subjects on the basis of measured heart rate. Using support vector machine, heart diseases are classified with mean-squared error of 0.023%. Two conditions, viz. normal and abnormal, are considered. The novelty of this paper is to use chaos analysis as an effective feature extraction tool for improving strength of healthcare professionals. The proposed technique shows detection error of 0.077%. The proposed method finds its major applications in regular screening of patient’s heart, heart dynamics observation during major heart therapy, etc.

Photovoltaic energy sources are the most reliable renewable energy sources. The major limitations of solar energy are its Weather-Dependency and availability at daytime only. As photovoltaic energy storage is very expensive hence it is smarter to use solar energy during the day and take energy from the grid during the night. So, it is a good solution to connect the solar power systems with the grid which is linked with an infinite bus. In this paper, a microgrid system, based on two photovoltaic generating stations and an infinite bus system that is capable to export or import power to the grid is simulated. The microgrid system investigated in this paper represents a study based on an eight-bus system. The output voltage of photovoltaic generation system and infinite bus with constant voltage is calculated in our study. Computation of bus voltage and power flow of the microgrid for green power importation or exportation to the local power grid is carried out using Newtown—Raphson algorithm.

As the amount of data is increasing rapidly, its storage and security issues also increase. Hadoop is currently a popular framework for big data storage and processing. Since, security is not a primary consideration in Hadoop when it was initially developed makes the data stored in Hadoop distributed file system (HDFS) vulnerable to attacks. For protecting these data, numerous encryption techniques has been developed. This paper analyses and presents a comparison on advanced encryption standard (AES), a symmetric cipher and Rivest-Shamir-Adleman (RSA), an asymmetric cipher cryptographic algorithms. Finally, this paper proposes a hybrid method that combines both RSA and AES technique to improve the performance of encryption-decryption on HDFS files and thereby increases the security of big data in Hadoop framework. The proposed technique is found to be feasible and is able to enhance the confidentiality of HDFS files stored in Hadoop framework and is also able to overcome the limitations when using the AES encryption technique alone.

Nowadays, the need for energy conserving in industries is in big rise. To meet the demand, various technologies have been adopted to fit industrial standards. This paper relies on the modern standard 4.o conventions used in critical industrial applications at each point. In Internet of Things (IoT), the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications network. In view of the reaction of ping production, idleness and throughput and analyzing their upgraded exhibition in companies such as oil and gas, energy utility, media transmission and some more industries are carried out. A number of application layer protocols have been considered in order to accommodate a wide range of applications. HTTP, a well-known, fundamental client–server protocol and the protocol that is most compatible with current network infrastructure, is one of the leading candidate solutions in this paper. In this paper, the Trial outcomes are acquired through demo industrial setup and recognized through Message Queuing Telemetry Transport (MQTT) and resultant checked through Wire shark and LCD in real-time.

The last two decades of the Information Age have been characterized by widespread proliferation of the Internet of things (IoT) technology. Besides diverse applications in various consumer, industrial, agriculture, and health care, IoT has enabled solutions for better management of natural resources. This paper illustrates the productive use of the IoT concept to automate the irrigation of vermicompost, encompassing three prime domains—waste management, smart irrigation, and (mobile) app. The soil moisture content and temperature of the compost bed are critical factors that delimit the earthworms’ life expectancy. This paper includes an intelligent monitoring system for effective irrigation of the compost bed, at precise time intervals. The irrigation status and the compost bed's moisture content can be monitored ubiquitously through the Amrita Sparsham mobile application software, minimizing human intervention, facilitating water conservation. Thus, the multipurpose solution is the convergence of Amrita waste management through vermicompost and the automated smart irrigation system monitored using Amrita Sparsham mobile application.

Legal information processing attracts attention from a number of organizations globally which includes research institutions; specific areas of interest ranges from representation of legal data, such as prior court cases, for countries which adopt common law system. These legal datasets typically include legislative acts or statutes, which vary from one country to another. Mining this legal data in order to extract useful information poses formidable challenges, which include coming up with ways for storing datasets, which are continuously generated and growing exponentially every year. Additional challenges include keeping up with amendments of the statutes and invalidating old statutes. This paper details a system architecture containing several key subsystems toward design and development of Legal Humanities for Africa, which is digital, query-able and easy to use and navigate by both lay user and experienced professionals. The Legal Humanities of Africa architecture has three modules, namely, knowledge base, knowledge engine and HCI module. The knowledge base handles the legal data dictionary, glossary and metadata in a domain-specific manner, while the knowledge engine handles processing data from the legal cases or statutes. Various approaches to computational linguistics, such as natural language processing or information extraction, have been used for natural language processing, including finding part of speech in text that contains most informative terms that are useful in computing similarity between prior cases to user queries. Submodules in knowledge base are also employed as needed to help optimize the process of extracting useful information to users usually in terms of relevant cases relating to specific legal matters at hand. Other techniques employed include several aspects of machine learning, such as unsupervised learning approaches to identify and cluster prior cases so that similar cases are clustered together, thus, making the process of matching user queries to prior cases easy. The HCI module provides user-specific (lay vs. experts), domain-specific and other anchor desks as required in a typical large application that can be commonly used by many types of users. A parametric evaluation of the performance of the Cloud-based Legal Architecture indicates that the system can enhance its use by both professionals and commoners; details are provided in this paper. It is hoped that the Legal Humanities of Africa architecture will become the benchmark architecture for Africa at large.

The paper studies the applications of multi-agent systems and sensors in healthcare. It goes through the current advancements in the field by introducing the literature and discussing applications using the technology in healthcare. The paper proposes a multi-agent system in a healthcare environment. It then conclude by discussing the merits and limitations of the technologies involved in the domain and their future scope.

In recent years, artificial intelligence technology has been widely used in the field of image classification of pneumonia. Although convolutional neural networks with deeper layers and more complex structures can classify images of pneumonia more accurately, it also brings problems such as network degradation and complex structure, which often require excessive system resources. Therefore, according to the characteristics of pneumonia images, this paper proposes an optimized residual network classification method. First, according to the characteristics of pneumonia medical images, the ResNet model with better classification effect in the deep network is selected, and the pre-processed pneumonia data set is used for sparse pre-training. Second, the channels in the network are pruned and P-ResNet is obtained model. Finally, the experimental results show that the P-ResNet18 model obtained after the pruning comparison in this paper is only 0.48% lower than the accuracy of P-ResNet50 by 0.905%, and the model parameters are reduced by 54.46%, which is much greater than that of P-ResNet50 and P-ResNet50. 47.78% and 45.72% of P-ResNet101, the reasoning time is reduced by 5.29%. The P-ResNet18 model can simultaneously ensure rapid and accurate preliminary screening and labeling of a large number of pneumonia chest radiographs.

Attention mechanism for action recognition is to enhance data by using attention mechanism so as to recognize human action in video. This research has been intensely investigated in the field of computer vision, which has attracted extensive attention from academia and industry. It is also the premise of intelligent interaction and human-computer cooperation, which helps the machine to perceive the external environment. In the past decade, especially the emergence of deep learning technology, great progress has been made in this field. Therefore, it is necessary to make a comprehensive review of the recent work. This paper summarizes the research background and application fields, typical datasets and experimental evaluation, and compares various attention mechanism for human action feature representation and action classification algorithms. Finally, we look forward to the development prospect of human action recognition in self-driving vehicles. This paper provides ideas for the follow-up research on the understanding of the behavior of self-driving vehicles, and makes self-driving vehicles become interactive wheeled robots.

This paper studies the quantitative measurement method of risk occurrence and possible spread among petrochemical supply chain enterprise nodes. Combined with the characteristics of petrochemical supply chain network, a risk propagation model of the supply chain based on cascading failure process is proposed, and a simulation experiment of risk propagation is carried out to investigate the different model parameters and different risk source enterprises on the communication process. According to experimental results, this paper puts forward targeted preventive measures to achieve the prevention and control of supply chain risk, which has important guiding significance for the regular operation of the petrochemical supply chain.

The graph neural network (GNN) can significantly improve the performance of few-shot learning due to its ability to automatically aggregate sample node information. However, many previous GNN works are sensitive to noise. In this paper, a few-shot image classification algorithm (Proto-GNN) based on the prototypical graph neural network is presented. First, convolutional neural network (CNN) is used to obtain the feature vectors of the support set samples, which can be used to calculate the prototype of each category. Then, the feature vectors of the support set, prototype vectors and the feature vectors of the query set are used to form a completely connected graph. Finally, Proto-GNN is built and trained. Due to the addition of the prototype vectors, the query set samples obtain more category information, which is more conducive to classification. The experimental results show that the model in this paper has a better classification performance on the Omniglot data set.

Tumor region segmentation in liver pathological images has become a significant tool of routine diagnosis and it also plays an important role in the liver cancer surgical plannings. Liver lesions are usually manually marked by clinicians on ultra-high resolution pathological images, which is awfully time-consuming and laborious. Aiming at this problem, we propose a new deep neural network for automatically segment liver cancer pathological images in this paper to assist doctors in completing the diagnosis efficiently. The existing U-Net framework was mainly used in previous researches, but not all features extracted by the encoder module are suitable for pathological image segmentation. In order to solve this problem, the network in this paper designs an attention-guided connection module to obtain accurate segmentation of liver cancer, so that the useful context features can be selected adaptively from the low-level features which are guided by the high-level features. The pyramid sampling operation is applied to the global attention module to fuse different levels of features. The results of experiments show that the method has some certain advantages over the previous methods in liver cancer pathological image segmentation datasets, and its IOU is increase by 5.4% and 2.2% compared with U-Net and PSPNet networks. Simultaneously, in order to testify the basic concept preponderance, some related studies about the ablation are carried out.