Reviewerly/exHarmony huggingface dataset:
Machine Learning based URL Analysis for Phishing Detection

Attacks that take place online most frequently take the form of phishing scams. Attackers attempt to gather user data without the consent of any such user through emails, URLs, and any other link that sends a viewer to the a dubious page where a consumer is persuaded to start taking specific actions that can successfully complete an attack. An attacker has the opportunity to gather vital information about the victim during these attacks, which they can use to presume the victim’s identity & carry out tasks that only the victim should have been able to carry out, such as making purchases, sending messages to the other people, and simply attempting to access the victim’s info. Many studies have been done to discuss potential defenses against these assaults. This study employs three algorithms for machine learning to ascertain whether such a web page is phishing. In the experiment, software that analyzes web page URLs to distinguish between legitimate & phishing websites is used to try to prevent attacks using these models which have been trained to utilize URL-based features. The accuracy, recall, and F1 Score of the random forest classifier’s performance from the observations were 97.5%, 99.1%, and 97.3%, respectively. The proposed model is quick and effective because, unlike earlier studies, it only relies on the URL and doesn’t conduct analysis using any other sources.
An Approach to Recognize Human Activities based on ConvLSTM and LRCN

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches’ heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.
Role of AI, Big data in Smart Healthcare System

Since AI and big data are considered the pinnacle of technological advancements this century, intelligent healthcare systems have recently embraced big data analytics and AI and this study analyzes the application of AI and big data analytics to improve the healthcare system. Several AI-based algorithms, big data frameworks and the methodologies used to look at the source. This study also explores the applications of these technologies for providing users with insights and aiding them in planning, notably for the specific challenges in innovative healthcare, and proposes a model based on AI and Big data Analytics for health. Intelligent healthcare procedures are now more commonly adopted. The information accumulated from patient treatment has been utilized to evaluate well-being-related designs and find practically identical cases. Enormous information investigation is fundamentally improving the medical services framework. The medical services industry delivers a monstrous measure of mind-boggling information continually changing and developing. With the assistance of previous information bases, large information investigation and mental registering empower the medical services framework to store and develop tremendous volumes of information for treating patients and ascertaining vulnerability. These vigorous arrangements reinforce these frameworks and increment their viability in the ongoing climate. An insightful of these framework needs a protected organization engineering and cloud settings to produce bleeding edge and dependable outcomes. In these conditions, a human guide will ordinarily be required. Just in emergency clinics or medical services establishments, might patients at any point go through broad therapies while being electronically observed. In different circumstances, patients can be dealt with online with the assistance of some economical observing hardware at home, diminishing the requirement for in-person excursions to the emergency clinic
Sentiment Analysis Techniques and their Uses: An Analytical Study

Sentiment Analysis has become a centre of attraction in context of natural language processing and text mining lately. Modern complex businesses use it as an effective marketing strategy where goods are traded over internet and buyers leave their feedbacks. The paper presents a systematic analysis of the highly referred methods and methodologies that are implemented to conduct Sentiment Analysis and recommends the most effective ways to use them. The paper takes an illustration from a Bollywood movie “Drishyam2’’ to employ Sentiment Analysis on the viewers.
Data Sovereignty Provision Blockchain for Remote Healthcare Service

Remote healthcare services provide more opportunities for communication between doctors and patients, strengthening their relationships and improving patient satisfaction and loyalty. However, those services face specific security and privacy challenges that constrain growth. Furthermore, patients’ information is scattered in several hospitals with various formats, which does not provide the data sovereignty of patients. Data sovereignty is a way for any service to control or regain its data. This paper proposes a privacy-preserving blockchain for remote healthcare services to solve these issues. There is only one database of patient electronic health records in our data structure, and various hospitals could use it. The proposed data structure keeps the patient’s electronic health records and the doctor’s diagnosis and prescription data separate. Each entity in our system has different access rights to its role. The proposed system is based on some security primitives, including pseudonym policy, delegation, data encryption, and digital signature, depending on the privacy requirements. The proposed blockchain could be a solution against the previous centralized system and provide remote healthcare services’ data sovereignty, privacy, and security.
Identifying Anti-Social Activities in Surveillance Monitoring Applications using Deep-CNN based Algorithms

Safety is the primary concern in present times. Crimes happen in public places and the criminal can quickly get away from the scene without anyone noticing him or any evidence against him. CCTV cameras are used for surveillance monitoring but they still need human supervision to operate and thus have a higher possibility of human error. So, in such cases, we need a machine to recognize such tasks and create evidence if it notices any such activity. Though many modern and advanced machine learning algorithms, processors, and CCTV cameras are available, but real-time detection is still difficult to achieve. Our work aims to create a system that identifies if any anti- social or abnormal activity is there or not from cluttered scenes. This works on Transfer Learning. We propose to use a Deep Convolutional Network (DCN), a state-of-the-art CNN model using the latest object detection technique YOLOv7. Using this in surveillance monitoring can be useful to reduce both the risk to human life and the rate of crime.
Machine Learning based Efficient Recommendation System

Recommender systems are under the domain of information filtering systems. The basic objective of a recommender system is to give the user software recommendations for things that might be useful. The advise can be used while making a range of decisions, such as selecting what to buy, what to watch on television, or where to travel. This work constructs and compares a large number of recommendation systems that may suggest products based on user, item, and user-item interaction data utilising a range of machine learning techniques. For the administration’s recommendation system, the information analysis difficulty has received a lot of attention lately. Network resources are fully connected and swiftly developed for clients. For the administration’s recommendation system, the information analysis difficulty has received a lot of attention lately. Network resources are fully connected and swiftly developed for clients. The suggested approach focuses on instruction, criticism, administration, reporting, configuration, and utilising it to provide the user with relevant information to support decision-making and data item recommendations.
Hybrid Machine Learning Technique for Prediction of Phishing Websites

Phishing is one of the most common types of use of cyberspace for criminal purposes. Phishing sites are websites that look and sound like legitimate ones but are really fraudulent. They are fabricated with the intent of duping the recipient into thinking the item is real. The phishing scams of today are more complex and dangerous than ever. Phishing website predictions may be made using machine and deep learning approaches based on artificial intelligence. A machine learning (ML) classification algorithm may be used to identify potential phishing websites. In this research, we describe a hybrid machine learning method for phishing site prediction.
Data Hiding using Image Steganography

Image Steganography is the artwork of concealing mystery data within the image such that the hacker will now no longer be capable of discover the records within inside the stego images. This is a useful approach to secure our sensitive information. Security has continually been a main difficulty from last many years to existing days. The topic of interest to researchers has long been the development of secure technologies for sending data to anyone other than the recipient without revealing it. Therefore, from nowadays, researchers have evolved many strategies to meet the steady transfer of information and steganography is one in all them. In this paper, we work on two techniques for hiding information in the image. First, we do analysis on LSB for storing information bit. As the technique is known to all, the attacker will be able to easily reveal the information, this makes image steganography unsecured. Secondly, R-Color Channel encoding with RSA set of rules for offering extra protection to information in addition to our information hiding approach. The proposed approach makes use of a red color channel for hiding information bits and the following bits for RGB pixel values of the original image. This paper present the performance analysis of two most popular algorithms, LSB and RSA along with image steganography.
Leader Election in Internet of Things Using Blockchain: Issues and Challenges

Now days, Internet of Things (IoTs) is the most promising area of research, for the problems, in order to create distributed and innovative solutions. An IoT connects large amount of heterogeneous devices in the network and manages. During the process, intruder may affect the data at any stage. Therefore, elegancy on data processing is the obvious requirements which can leads to timely and fair decision. In this direction, the present paper proposes the scope of leader election requirements for IoT networks. The leader election is a method to identify a privilege node from the connected components which can handle the coordination and control the systems. Our aim is to look into the problem using blockchain technology. The blockchain technology may help to select safe and secure leader for IoT. The research includes various design constraints of the leader election in IoT using blockchain technology. Also, our works cover mapping of blockchain with IoT environment and discusses related issues and challenges.
Light-Weight Deep Learning Model for Human Action Recognition in Videos

Human Action Recognition (HAR) from a visual stream has recently attained much researcher consideration in the domain of computer vision. Due to its large applications like monitoring of health, home automation, and tele-immersion, among others. However, it still faces human variances, occlusion, lighting changes, and complicated backgrounds. The evaluation criteria rely on the features collection approach as well as learning data being performed correctly. The success of Deep Learning (DL) has resulted in a variety of impressive outcomes, including neural networks. Nonetheless, a robust features vector is required for an efficient classifier to give the class label. Features serve as the essential component of any data set. Indeed, feature extraction may affect the algorithm’s performance and computational cost. For this research framework, we used pre-trained deep learning models VGG19, Dense Net and Efficient Net for feature extraction from the sequence of images and classified each action with the help of the SoftMax layer. UCF50 action dataset used, which contains 50 sections and evaluates performance with the help of precision, recall, f1-score and AUC score. Testing accuracy from models achieved VGG19-90.11, DenseNet-92,57 and EfiicientNet-94.25.
Load balancing in Cloud Computing Environment using Modified Genetic Algorithm

Cloud Computing has brought significant changes in the field of computing. Load balancing is a method by which the distribution of dynamic workload to various virtual machine in a host can be done so that particular host or machine will not be overloaded while other being ideal. Cloud Load Balancing has always been the crucial area to work and to come up with an approach that also has to take care of the constraints such as processing power, limited storage, bandwidth, etc. The aim of this work is to reduce in total response time and total execution time with the use of modified Genetic Algorithm. The method proposed will effectively schedule the cloudlets of the particular Virtual Machine with less loaded host in datacenter. The proposed method is implemented in the Cloudsim simulator and outperformed many static existing algorithms like First Come First Serve (FCFS), Round Robin (RR), Shortest Job First (SJF) and existing traditional Genetic Algorithm.
AttnHAR: Human Activity Recognition using Data Collected from Wearable Sensors

In recent times, there has been a massive surge in demand for wearable sensing devices which accurately decode human activities. These sensors are extensively used in smartphones and smartwatches. There are a wide variety of applications of human activity recognition such as surveillance through video, healthcare, virtual reality. In this paper, we propose a hybrid deep learning architecture that learns the relation between important time points by self-attention and extracts spatio-temporal features from time-series data. The proposed approach is validated on 3 public datasets to show that self-attention enhances the predictive abilities of a neural network, namely MHEALTH, USCHAD and WISDM. We also compare the proposed model with previous works on these datasets. The result analysis show that our model performs better on these datasets achieving an overall accuracy of 95.04%, 90.91% and 99.02% respectively.
Exploring the Techniques for Crowd Video Summarization: A Comprehensive Survey

Technologies for crowd video summarization aim to create an efficient and comprehensive summary by selecting the most significant parts of the video content. It aims to automatically generate a simplified version of an input video that contains only the most relevant or interesting parts. Numerous approaches have been developed in the past few decades and the state-of-the-art is exemplified by methods utilizing architectures. This research examines the current progress made in the field and presents a thorough overview of existing crowd video summarization methods. In this paper, we present our motivation for exploring the development of technologies for crowd video summarization and discuss the task, characteristics, and taxonomy of existing algorithms. We provide a systematic review of related literature and report on protocols for objectively evaluating crowd video summarization algorithms by comparing the performance of several approaches. The results of these comparisons, as well as some documented considerations about the amount of annotated data and appropriate evaluation protocols, are used to suggest possible research directions for future work.
Key Factors Related to Cyber Security Affecting Consumer Attitude in Online Shopping: A Study in Bangladesh

Cybersecurity is an increasingly important factor in consumer attitudes toward online shopping. Online shopping has become an essential part of our lives in this digital era. As the popularity of online and e-commerce shopping continues to grow, so does the potential for cyber threats and attacks. As more and more consumers turn to online shopping, cyber threats such as hacking, identity theft, and credit card fraud have become more frequent. Therefore, understanding the factors of cybersecurity that affect consumer attitude is essential to build trust and creating a safe and sound shopping environment. This research explores the factors of cybersecurity that affect consumers’ attitudes to shopping online and uses a survey to test several hypotheses related to influential cyber factors. Bangladesh is a developing country in Southeast Asia, and like many other countries, has experienced an increase in cyber threats and attacks in recent years. Consumers in Bangladesh face many of the same cyber threats, such as phasing attacks, malware, data breach, and other types of cyber security threats over online shopping. As a result of these cyber threats, online consumers are increasingly concerned about online security risks which may impact their willingness to engage in online shopping. Therefore, it is essential to identify critical factors of cyber security that impact consumers’s attitudes toward online shopping to mitigate cyber risk and improve consumer trust in online shopping. This paper provides the result of a research study that will provide a better understanding of factors that influence consumer’s trust and engagement with online and E-commerce platforms in Bangladesh)
A Survey of Sarcasm Detection Techniques in Natural Language Processing

Sarcasm is a linguistic style that is often employed in regular conversation and that natural language processing (NLP) systems may find difficult to recognize. The use of sarcasm in social media, online reviews, and other digital communication has increased in recent years, making it essential for NLP systems to detect sarcasm accurately. In this survey, we provide an overview of the current state of the art in sarcasm detection using NLP techniques. We discuss the various approaches to detect sarcasm, including machine learning, deep learning, and lexicon-based methods. We also review recent research on sarcasm detection in various languages and contexts, such as social media, customer reviews, and online forums. We also identify opportunities for future study and address the difficulties and limits of the present sarcasm detection techniques. The overall goal of this survey is to further this field of study by providing a thorough grasp of sarcasm detection in NLP.
A Comparative Study of Various Learning Models for Object Detection in Contextual Scene Interpretation

Deep Learning approaches for solving the problem created a revolution in the field of research. An example like scene interpretation which includes the objects detection, classification, and recognition of objects. These deep learning techniques can handle large amounts of structured or unstructured data. This paper shows the comparative analysis of various model which are most preferable in deep learning for the object detection and classification purpose. The model like CNN, YOLO, RNN, RCNN, Fast RCNN, and Faster RCNN is used for object detection and classification purpose with various datasets like Imagenet, PASCAL, and SYSU. RNN model is also used to show the relation between the objects. This paper covers various models of deep learning techniques for detecting and classifying objects from the scene.
Implementation and Comparison of Artificial Intelligence Techniques in Software Testing

AI is a prominent innovation of the current digital world. It powers most of the cutting-edge digital devices. It is a blend of unique technologies from machine learning (ML) to mixed reality (MR), and more. AI has significantly touched many sectors including healthcare, life sciences, manufacturing, retail, and agriculture. AI, as many expect, is going to be the future of the computing world. Artificial Intelligence has been a boon to the software industry as well. The software testing area has been highly touched by AI. Any applications that are built, need to be tested before providing it to the client. It becomes tedious if it’s a complex timebound application. Manual testing in those scenarios doesn’t seem to be a feasible solution. Although test automation has been incorporated in many companies, they aren’t efficient in all cases. AI, on the other hand, helps test the applications effectively. Machine learning and deep learning technologies of AI are playing a crucial role in training and inferring massive amounts of data resulting in faster testing of the application. In this paper, we discuss the implementation and comparison of machine learning and deep learning techniques in Software Testing.
Speech reading ability in class 3 students MI Nurul Ikhsan

Background: Reading is one of the important aspects that must be mastered by students, speech is also an important aspect in this study because it is to train students' confidence from an early age. Purpose: This study aims to determine the ability to read speech in grade 3 students of MI Nurul Ikhsan. Design and methods: This research is using quantitative descriptive methods. Results: The results of this study indicate that students who get the very good category are 10.5% for those who get the good category are 24%. which is commonly obtained by students, namely in the less good category as much as 31%. From the results of the study, it can be concluded that the ability to read speech in grade 3 students can be said to be not good.