Reviewerly/exHarmony huggingface dataset:
An Unsupervised Learning Algorithm based deletion of Inter-frame Forgery Detection System

People utilize contemporary tools to edit captured video clips effortlessly. These video clips hold significance as evidence within a legal setting. Consequently, the identification of video tampering becomes crucial during legal proceedings. In order to improve the legal process, this paper presents an inter-frame forgery detection system especially designed for frame deletions. The system initially receives video clips and employs a statistical approach to determine whether they have been tampered with or are authentic. If the video system is determined to be forged, it will proceed with detecting frame deletions or other alterations. This detection process utilizes an unsupervised learning algorithm, specifically the K-mean learning algorithm. By accepting the cross-correlation coefficient value, the detection process categorizes the different types of forgery. The conducted tests on video clips sourced from two data sets: the University of Central Florida (UCF) 101 data sets and Surrey University Library for Forensic Analysis (SULFA) data sets to evaluate the system. The outcomes of the conducted investigation clearly showcase the effectiveness of the suggested system in precisely identifying instances of frame deletion forgery, achieving an impressive success rate of 87%.
Detection and Analysis of Port Scanning and SQL Injection Vulnerabilities with correlating factors in Web Applications to Enhance secure Data Transmission

The increasing use of web applications has led to a rise in vulnerabilities, with SQL injection being one of the most prevalent and harmful attacks. Attackers exploit flaws in how web applications handle user input to manipulate database queries and gain unauthorized access to sensitive data or perform destructive actions. This article aims to explore the working of SQL injection attacks and highlight the role of port scanning in such attacks. By understanding the causes and mechanisms behind SQL injection, we can better comprehend the importance of proper input validation and parameter handling in securing web applications. The study reveals that web applications vulnerable to SQL injection can be exploited through port scanning to identify open or closed ports, like MySQL's default port 3306. Attracting malicious SQL code into user input fields can alter database queries and potentially compromise the application's security. SQL injection remains a significant threat to web applications due to poor input validation and inadequate handling of parameters in SQL queries. Proper security measures, such as input validation and parameterization, are crucial to mitigate the risk of SQL injection attacks and enhance overall application security.
Dementia Care Using AI: Real-time Patient Trajectory Monitoring System

Dementia, characterised by cognitive decline, has emerged as a significant global health concern, with a new case diagnosed every 3 seconds worldwide. This study addresses the challenges faced by dementia carers through an innovative approach that combines Progressive Web App (PWA) technology with machine learning and GPS trajectory analysis, customised to aid in care. The proposed method identifies and addresses gaps in existing research, aiming to improve the lives of individuals in the early stages of dementia and their carers by offering real-time anomaly detection and alerts based on patient trajectory data. The methodology encompasses several pivotal stages: Data acquisition and transmission involve collecting and relaying patient trajectory data to the cloud; the isolation forest technique, a sophisticated anomaly detection method, identifies deviations from normal behaviour within patient trajectories. Real-time anomaly detection and alert generation provide swift notification to carers in the event of abnormal patient movements. The approach also encompasses carer engagement and response dynamics. Carers play a crucial role in determining the veracity of detected anomalies and responding accordingly. Based on carer feedback, the system adapts and updates its algorithm for symptom detection and threshold setting. In conclusion, the integrated strategy presented in this study offers an innovative and effective approach to enhancing the quality of care for individuals with dementia and their carers, paving the way for enhanced patient monitoring and carer engagement.
VM Consolidation Algorithm for Energyefficiency and Quality of Service

Scheduling is undoubtedly one of the difficulties that the cloud, which is always developing, must overcome. Scheduling describes a specific method of arranging the tasks that a laptop device should complete in the correct order. In this study, we used FCFS and round-robin scheduling to construct a standard priority seek set of criteria for assignment execution and assessment. The method has been tested on the cloud, and the outcomes demonstrate that it outperforms several traditional scheduling techniques. In distributed computing systems, a variety of scheduling techniques are used, task scheduling being one of them. For resource optimization, we specifically cover three methods: ANT colony, analytic, and genetic algorithms. We developed a new precedence set of rules with a limited number of activities; in the future, we will take on additional jobs and work to reduce the implemented execution time. We may also expand this set of rules to the network environment and examine the time difference between the cloud and the network. The suggested approach is entirely based on queuing models. The burden, response time, and length of the common queue were all decreased by directing incoming requests to a low task. These results show that our version may increase the utilization of the global agenda while decreasing latency. The results of the experiments verified that the suggested version may lower power consumption, hence enhancing service quality inside the cloud architecture. Future iterations of the planned version would utilize cloud computing techniques built on parallel algorithms to speed up activation of user requests. We recommend the adoption of a heterogeneous resource allocation method called Skewness avoidance multi-resource allocation (SAMR) to distribute resources based on particular requirements for particular types of assets. Our approach comprises of a set of VM provisioning guidelines to ensurethat heterogeneous constraints are properly distributed to prevent resource overload across PMs and a model-based method to determine the most energetic mix of PMs SAMR should be running on. We will demonstrate the relatively low complexity of our model method for accurate and useful evaluation work. Many simulation results demonstrate SAMR's superior efficacy and overall performance to competitors.
Automatic weapon detection using Deep Learning

Safety and security is important in today’s world scenario. For a country to be strong in terms of economy it must have secure and safe environment. CCTV surveillance is majorly used to monitor activities such as robbers and threats in public places, but these cameras need constant human observation. The fast and accurate automatic weapon detection system is useful to avoid these types of risks in public places such as schools, hospitals, museum and traffic etc. In many places the crimes are caused by pistols, guns and knives are very more, mainly in the countries and cities where there is no gun control laws. This work, focuses on automatic weapon detection in CCTV footage by making use of deep learning algorithms. In our work we are going to use YOLOv8 model to detect weapons as it is better in terms of accuracy and speed when compared to other models. It uses larger data set which consists of various images of weapons. Data set includes pictures from youtube robbery videos, CCTV footages, GitHub repositories and Internet movies firearms data base. Sliding window technique is used to extract features from objects and classify weapons. YOLOv8 has shown improvement in detection with greater speed and high accuracy.
Unleashing Deepnets to Combat ad Click Fraud and Safeguarding Online Advertising

As online advertising continues to be a significant revenue source for businesses, the threat of ad click fraud poses a substantial challenge to the legitimacy and effectiveness of these campaigns. Adversarial actors engage in ad click fraud to generate false clicks on ads, leading to financial losses for advertisers and misleading performance metrics. This paper proposes a novel approach to combating ad click fraud using Deep Neural Networks (Deepnets). By leveraging the power of Deepnets, which excel at pattern recognition and anomaly detection, advertisers can develop robust fraud detection systems that identify and mitigate fraudulent click activities. The proposed method utilizes historical click data to train Deepnets, enabling them to learn the complex relationships between various attributes associated with genuine and fraudulent clicks. Through extensive experimentation, the research demonstrate the effectiveness of Deepnets in accurately distinguishing between legitimate and fraudulent clicks, thus safeguarding online advertising budgets and enhancing campaign outcomes. This research contributes to the practical application of deep learning in the field of online advertising security and establishes a foundation for further advancements in fraud detection techniques.
A Novel Approach for Image Background Elimination

A convenient and effective mobile tool for enabling the painless removal of background from photographs is the Image Background Remover Android Application. The essential job of background removal in image analysis and computer vision has applications in computer graphics, object detection, and image editing. This study compares and contrasts cutting-edge background removal algorithms and approaches in order to assess their benefits, drawbacks, and suitability for various uses. The application enables users to get precise and eye-catching results by utilizing cutting-edge algorithms for image processing and interactive features. Users using the app can upload pictures from the device’s storage or take brand-new ones with the camera. Then, a variety of background removal techniques are used. Then, to precisely distinguish between foreground and background regions, a variety of background removal techniques are used, such as color-based thresholding, Grab-cut and semantic segmentation based on deep learning. Using interactive editing tools, users have the freedom to manually improve the segmentation. The program additionally supports real-time processing, enabling users to see a live preview of background removal. Users may easily generate captivating photographs with smooth backgrounds removed thanks to the results being displayed through an easy and responsive user interface. The application shows its effectiveness, adaptability, and promise in practical uses in processing images and artistic production of content through careful analysis and user input.
Blockchain Enabled Real Estate Property Transactions using NFT: An Approach

Real estate is an asset class traditionally associated with complex transactions and lengthy paperwork. The emergence of blockchain technology, Non-Fungible Tokens (NFTs), and smart contracts help the real estate industry transactions faster, more secure, and more transparent. This study proposes an integrated framework for revolutionizing property transactions by leveraging blockchain technology, ensuring heightened security and transparency. Here we use NFTs to represent properties which makes it easier to track changes in ownership and ensure that all parties involved in a transaction clearly understand who owns the property and when it was transferred. It also extends the application of cryptographic principles, introducing zero-knowledge proofs to validate property ownership. This innovative approach enables parties involved in property transactions to verify the authenticity of ownership without disclosing sensitive information. Through a combination of theoretical analysis, technological exploration, and real-world considerations, this paper lays the groundwork for a more secure, efficient, and privacy-aware landscape for property transactions.
Revenue Forecasting Using Distributed Machine Learning In Online Advertising Systems

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation.
Systematic Literature Review on Early Diagnosis of Oral Squamous Cell Carcinoma by Deep Learning Techniques

Oral Squamous Cell Carcinoma (OSCC) is the seventh most prevalent type of cancer in the neck and head. The prognosis and survival rate of the patient is significantly improved by early identification of OSCC. Due to tumor heterogeneity, such a diagnosis requires much time and a high-efficiency human experience. As a result, artificial intelligence systems assist professionals and physicians in making precise diagnoses. Recent advances in computer vision-based techniques and Computational Intelligence (CI) improve accuracy in medical images. This study aims to develop hybrid methodologies based on fused features to produce excellent outcomes for the early detection of OSCC. This systematic review aims to estimate deep learning (DL) based algorithms for early diagnosis OSCC to assist clinicians in oral cancer diagnosis and screening. The terms "squamous cell carcinoma," "early diagnosis," "oral cavity," "histopathological image," "biomarker," "Optical Coherence Tomography (OCT) image," and "deep learning" were used in a Google Scholar Cochrane and MEDLINE (PubMed), Embase and WoS databases (January 2018 to July 2023) to find relevant articles. The inclusion criteria were the use of deep learning approaches for early diagnosis of OSCC, articles older than 5 years, and publications written in English. Case reports and studies written in foreign languages met the exclusion criteria. 70 publications were chosen to be included in the systematic review out of the 194 studies that were initially found through the search. Deep learning techniques based on hybrid features are examined in evaluating performance metrics and superior results of the present systems employing biomedical images for OSCC diagnosis. It has been established that the deep learning-based early identification method for biomedical images has the ability to offer decision support for efficient oral cancer diagnosis and screening.
3-Dimensional Indian Dance Pose Classification using Convolution-al Neural Network

The classification of Indian classical dance is complex because it contains complex human body parts. The Identification and recognition of classic dance is a challenging task due to every dance gesture consisting of tough single-hand, double-hand, body movements, leg movements, facial emotions and background Music. In this work, we have tried five different songs, every song consists of thirty to forty categories. For the identification and classification of dance, we have proposed a convolutional neural network, which performs better than various state-of-the-art works. The inputs to the proposed model were captured from Microsoft Kinect, we analogize and separate machine learning models and deep learning models. The achieved recognition score of our work is 95.63
Bone Marrow Cell Classification Using Efficientnet B5 Transfer Learning Model

In order to diagnose and treat a wide range of bone marrow (BM) monitor is a extremely important step. The most important and essential information is contained in the BM nucleated differential count (NDC) analysis, which is a component of the analysing of BM. In this work, the capability of a trained CNN model to classify images of BM cells is evaluated. Therefore, it is crucial to create a deep learning automated categorization system for BM cells. Traditional classification artificial intelligence algorithms, however, only generate classification results. The outcomes of the study demonstrate that the proposed method has an accuracy of 96.72%. If several BM cell types are analysed, this will be useful for tracking conditions including thrombocytopenia, polycythaemia vera, anaemia, as well as some cancers like leukaemia, lymphoma, and multiple myeloma. It helps to increase the healthy quality of life. It can significantly increase the efficiency and precision of identifying BM cells.
Transfer Learning for Accurate Classification of Breast Cancer in Medical Imaging

Transfer learning has recently been developed as a powerful technique for accurate classification of medical images. It is predominantly used in deep learning models to facilitate training of models on small data sets. It is based on the process of leveraging the knowledge gained from prior related tasks and transferring it to a new task. This technique can be used to improve the accuracy of classification models trained on medical images, specifically for the classification of breast cancer. Such models are able to provide an improved accuracy of cancer classification compared with those trained in a standard fashion. Additionally, transfer learning models demonstrate the ability to increase computational efficiency, reduce over fitting, and construct useful representations from data with fewer annotations. This technique is particularly useful for medical imaging due to the expense and difficulty in acquiring large annotated datasets for training purposes. This paper explores the use of transfer learning for accurate classification of breast cancer in medical imaging, and its potential applications in the diagnosis of this disease..
Assessing the Accuracy and Ethical Implications of Automated Criminal Detection Using a Deep Learning Approach

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases.
Web Extension For Phishing Website Identification: A Browser-Based Security Solution

With the rise of internet-based scams and cyberattacks, phishing remains one of the most prevalent threats to online users. In response to this ongoing challenge, this research paper presents the design and implementation of a Web Extension to enhance user security by identifying and alerting users to potential phishing websites in real time. Web Extension utilizes a combination of phishing detection techniques, including URL analysis, SSL certificate verification, and machine learning algorithms. By intelligently analyzing website URLs, the extension can detect suspicious patterns, misspellings, or anomalous subdomains commonly associated with phishing attempts. It further verifies the authenticity of SSL certificates to ensure a secure connection between users and websites. To enhance its accuracy, Web Extension can also leverage external APIs or databases containing known phishing websites. This cross referencing enables prompt identification of blacklisted websites and prevents users from inadvertently falling victim to phishing scams.
Smart Performance Optimization of Natural Language Processing with Recursive Neural Networks

Recursive Neural Networks (RNNs) are a powerful tool for Natural Language Processing (NLP) that can easily learn and represent recursive structures in sentences. Artificial Neural Networks (ANNs) have been widely used in various NLP tasks, such as language modeling, text classification, and sentiment analysis. With the development of deep learning, RNNs have emerged as a powerful tool to represent structural and syntactic information of sentences. RNNs have attracted a lot of attention due to their ability to learn complex language structures and representation. Consequently, RNNs have achieved great performance in a variety of NLP applications, such as text summarization, machine translation, and question answering. In this paper, we discuss the importance of RNNs in NLP and present different smart performance optimization strategies for RNNs. Firstly, we consider the use of pre-trained language models such as GloVe and word2vec to enhance the representation of words and improve the overall accuracy. Secondly, we implement a variety of regularization techniques, such as dropout and batch normalization, to regularize the learning process of RNNs. Finally, we discuss the approaches used to reduce the complexity and overfitting of RNNs. We compare the performance of different RNNs on the Penn Treebank dataset with different optimization strategies. Experimental results show that our proposed approaches can significantly improve the performance of RNNs and provide a more effective solution for NLP tasks.
Bitcoin Price Forecasting: A Comparison of LSTM and Feedforward Neural Network

Technology and finance have experienced a change which leads to the rise of cryptocurrencies, with Bitcoin serving as a pioneer. Investors, researchers, and fans all share a fascination with bitcoin because of its decentralized structure and cryptographic security. The price volatility of Bitcoin has attracted attention and presents opportunities as well as difficulties for traders and analysts. For navigating this turbulent market, precise price prediction models are essential. In order to anticipate Bitcoin prices, this work compares Long Short-Term Memory (LSTM) with Feedforward Neural Networks (FFN). The work assesses the prediction ability of these two neural network architectures using historical pricing data and maybe other relevant factors. The comparison covers issues with anticipating Bitcoin price movements' accuracy, resilience, and generalizability.