Journal of Machine and Computing最新文献

{"title":"The Future of Neurodiagnosis: Deep Learning for Earlier Intervention","authors":"Rajkumar Govindarajan, Thirunadana Sikamani K, Angati Kalyan Kumar, Komal Kumar N","doi":"10.53759/7669/jmc202404075","DOIUrl":"https://doi.org/10.53759/7669/jmc202404075","url":null,"abstract":"This study presents an innovative deep learning framework for improved early detection of a debilitating neurodegenerative condition marked by cognitive decline and memory impairment. Timely diagnosis is crucial for effective interventions and improved patient outcomes. Our framework integrates diverse data sources, including structural and functional neuroimaging (MRI and PET) alongside clinical information, to enhance detection precision. Convolutional Neural Networks (CNNs) analyze structural MRI scans, extracting subtle changes in brain structure indicative of early disease progression. Functional insights are gleaned from PET scans, contributing to increased sensitivity. Additionally, longitudinal data is incorporated through Recurrent Neural Networks (RNNs) to capture the disease's temporal evolution. Training on a diverse dataset utilizes transfer learning, optimizing performance even with limited labeled data. Rigorous validation consistently demonstrates the model's effectiveness, achieving a 92% accuracy rate.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network","authors":"Sambhaji Marutirao Shedole, Santhi V","doi":"10.53759/7669/jmc202404076","DOIUrl":"https://doi.org/10.53759/7669/jmc202404076","url":null,"abstract":"Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clickbait Detection for Amharic Language using Deep Learning Techniques","authors":"Rajesh Sharma R, Akey Sungheetha, Mesfin Abebe Haile, Arefat Hyeredin Kedir, Rajasekaran A, Charles Babu G","doi":"10.53759/7669/jmc202404058","DOIUrl":"https://doi.org/10.53759/7669/jmc202404058","url":null,"abstract":"Because of, the increasing number of Ethiopians who actively engaging with the Internet and social media platforms, the incidence of clickbait is becomes a significant concern. Clickbait, often utilizing enticing titles to tempt users into clicking, has become rampant for various reasons, including advertising and revenue generation. However, the Amharic language, spoken by a large population, lacks sufficient NLP resources for addressing this issue. In this study, the authors developed a machine learning model for detecting and classifying clickbait titles in Amharic Language. To facilitate this, authors prepared the first Amharic clickbait dataset. 53,227 social media posts from well-known sites including Facebook, Twitter, and YouTube are included in the dataset. To assess the impact of conventional machine learning methods like Random Forest (RF), Logistic Regression (LR), and Support Vector Machines (SVM) with TF-IDF and N-gram feature extraction approaches, the authors set up a baseline. Subsequently, the authors investigated the efficacy of two word embedding techniques, word2vec and fastText, with Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) deep learning algorithms. At 94.27% accuracy and 94.24% F1 score measure, the CNN model with the rapid Text word embedding performs the best compared to the other models, according to the testing data. The study advances natural language processing on low-resource languages and offers insightful advice on how to counter clickbait content in Amharic.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Network Anomaly Intrusion Detection with IoT Data-Driven BOA-CNN-BiGRU-AAM -Net Classification","authors":"Suresh G, Sathya M, Arthi D, Arulkumaran G","doi":"10.53759/7669/jmc202404073","DOIUrl":"https://doi.org/10.53759/7669/jmc202404073","url":null,"abstract":"Network security is one of the key components of cybersecurity anomaly intrusion detection, which is responsible for identifying unusual behaviours or activities within a network that might indicate possible security breaches or threats. In this suggested intrusion detection system (IDS), network traffic data is continuously monitored via anomaly detection. The study makes utilising one of the most recent datasets to spot unusual behaviour in networks connected to the Internet of Things, the IoTID20 dataset, to facilitate this process. The preprocessing stage involves painstaking steps for smoothing, filtering, and cleaning the data. The Pine Cone Optimisation algorithm (PCOA), a novel optimizer inspired by nature, is introduced in this study for the feature selection process. PCOA seeks to increase the effectiveness of feature selection while drawing inspiration from the various ways that pine trees reproduce, such as pollination and the movement of pine cones by animals and gravity. Moreover, IDS is classified using Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Based on Convolutional Neural Networks (CNN-BiGRU-AAM), which makes use of deep learning's capabilities for efficient classification tasks. In addition, this work presents the Botox Optimisation Algorithm (BOA) for hyperparameter tuning, which is modelled after the way Botox functions in human anatomy. BOA uses a human-based method to adjust the hyperparameters of the model to attain the best accuracy. The results of the experiments show that the suggested methodologies are effective in improving network anomaly intrusion detection systems, with a maximum accuracy of 99.45%.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Efficient Data Aggregation in Wireless Sensor Networks Using Meta Heuristic Based Feed Forward Back Propagation Neural Network Approach","authors":"Navjyot Kaur, Vetrithangam D","doi":"10.53759/7669/jmc202404062","DOIUrl":"https://doi.org/10.53759/7669/jmc202404062","url":null,"abstract":"Sensor nodes are low-cost, low-power, tiny devices that make up the majority of WSNs, or distributed, self-organizing systems. These sensor nodes are able to exchange, perceive, and interpret data. The sensor nodes are equipped with a wide variety of sensors, such as chemical, touch, motion, temperature, and weather sensors. Because of its adaptability, sensors are used in a variety of applications such as automation, tracking, monitoring, and surveillance. Despite the enormous number of sensor applications, WSNs continue to suffer from common challenges like as low memory, slow processing speed, and short network lifetime. The feed forward back propagation neural network mode (FFBPNN) based on meta heuristics aims to create many paths for effective data aggregation in wireless sensor networks. This model handled the process of identifying and selecting the optimum route path. The distributed sensor nodes are utilized to create the various route paths. In this research paper, data aggregation is done using meta-heuristic firefly algorithm that helped in identifying an optimal route from among the found routes. After selecting the operative ideal route choice, the data aggregation procedure practices a rank-based approach to accomplish lower latency and a better packet delivery ratio(PDR). In addition to throughput, simulation was done to improve and measure performance in terms of packet delivery ratio, energy consumption, and end-to-end latency.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on Fume Hood Improvements for Energy Savings and Minimum Face Velocity","authors":"YoungJin Son, Sungmin Bae","doi":"10.53759/7669/jmc202404064","DOIUrl":"https://doi.org/10.53759/7669/jmc202404064","url":null,"abstract":"A fume hood is a device that safely exhausts harmful substances generated during the experimental process to the outside, and for the safety of researchers and the protection of experimental samples, the balance between the amount of air flowing in and the speed at which it is discharged to the outside is extremely important. The Face velocity of the fume hood is recommended to be 0.5m/s (ASHRAE 110-2016) specified by the American National Standards Institute (ANSI). The Korea Occupational Safety and Health Corporation specifies 0.4 m/s (KOSHA-G-7-1999). To increase the functionality of the fume hood, it must be operated 24 hours a day while maintaining the prescribed Face velocity to make a safe laboratory environment. However, constantly powering them consumes a significant amount of electrical energy. Therefore, it's crucial to make efforts to reduce energy consumption. Some labs have adopted a method to minimize electrical usage by powering the hoods only when they're in use. In that case, it should be done with great caution. Because hazardous substances inside the hood could be leaked, putting the entire lab at risk. By implementing such measures, organizations and institutions managing lab facilities can effectively reduce energy consumption while ensuring the use of safe fume hoods. Additionally, there's a growing trend in research facilities to maintain Face velocity of fume hoods below 0.25 m/s as part of efforts to decrease and conserve electrical energy. This study stems from preliminary research aimed at achieving a stable face velocity in fume hoods. In this study, to reduce the face velocity of the fume hood, we changed the structural design of the fume hood based on existing research. As a result, the face velocity of the fume hood is reduced from 0.5 m/s to 0.297 m/s. the study reduced the face velocity to a maximum of 0.297m/s. To verify the performance of the fume hood with minimum face velocity, we tested the ventilation performance with the international standard test method. It proves that the fume hood with face velocity 0.297 m/s make no differences compared to existing fume hood with higher face velocity. In addition, the fume hood with minimum face velocity enables the reduction of energy consumption.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Real-Time Human Activity Recognition via Innovative Fusion of 3DCNN and ConvLSTM Models","authors":"Roopa R, Humera Khanam M","doi":"10.53759/7669/jmc202404071","DOIUrl":"https://doi.org/10.53759/7669/jmc202404071","url":null,"abstract":"Object detection (OD) is a computer vision procedure for locating objects in digital images. Our study examines the crucial need for robust OD algorithms in human activity recognition, a vital domain spanning human-computer interaction, sports analysis, and surveillance. Nowadays, three-dimensional convolutional neural networks (3DCNNs) are a standard method for recognizing human activity. Utilizing recent advances in Deep Learning (DL), we present a novel framework designed to create a fusion model that enhances conventional methods at integrates three-dimensional convolutional neural networks (3DCNNs) with Convolutional Long-Short-Term Memory (ConvLSTM) layers. Our proposed model focuses on utilizing the spatiotemporal features innately present in video streams. An important aspect often missed in existing OD methods. We assess the efficacy of our proposed architecture employing the UCF-50 dataset, which is well-known for its different range of human activities. In addition to designing a novel deep-learning architecture, we used data augmentation techniques that expand the dataset, improve model robustness, reduce overfitting, extend dataset size, and enhance performance on imbalanced data. The proposed model demonstrated outstanding performance through comprehensive experimentation, achieving an impressive accuracy of 98.11% in classifying human activity. Furthermore, when benchmarked against state-of-the-art methods, our system provides adequate accuracy and class average for 50 activity categories.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined Feature Set with Logistic Regression Model to Detect Credit Card Frauds in Real Time Applications","authors":"Prabhakaran N, Nedunchelian R","doi":"10.53759/7669/jmc202404074","DOIUrl":"https://doi.org/10.53759/7669/jmc202404074","url":null,"abstract":"Online payment methods are gaining popularity and are widely used, both in-store and online. Because to the Internet and smart mobile devices, conducting such transactions is quick, simple, and stress-free. However, online payment fraud is common due to the open nature of the internet, which allows criminals to use techniques such as eavesdropping, phishing, infiltration, denial-of-service, database theft, and man-in-the-middle assault. Online payment fraud is on the rise, and it is a big contributor to global economic losses. Financial services, healthcare, insurance, and other industries have long been plagued by fraud. Online fraud has developed in tandem with the use of digital payment systems such as credit/debit cards, PhonePe, Gpay, and Paytm. Furthermore, fraudsters and criminals are adept at evasion strategies, allowing them to steal more. Developing a secure system for client authentication and fraud protection is tough since there is always a workaround. This means that fraud detection systems play an important role in preventing financial crimes. Over time, victims of internet transaction fraud have incurred tremendous financial losses. The growth of cutting-edge technologies and global connection has led to a surge in online fraud. To reduce these expenses, it is critical to develop effective fraud detection systems. Machine learning and statistical tools make detecting dishonest money deals much easier. The scarcity of data, the sensitive nature of the data, and the uneven class distributions make it challenging to implement efficient fraud detection models. Given the delicate nature of the information, it is difficult to draw conclusions and construct more accurate models. This study offers a Linked Feature Set with Combined Feature Set with Logistic Regression (CFS-LoR) Model for accurate detection of online payment frauds. In comparison to extant models, the proposed model exhibits a highly accurate detection capability.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes","authors":"Deepthi S, Mamatha Balachandra, P. K V, K. Yau, Abhishek A K","doi":"10.53759/7669/jmc202404059","DOIUrl":"https://doi.org/10.53759/7669/jmc202404059","url":null,"abstract":"In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency","authors":"Nabeel S. Alsharafa, S. Shanmugam, Bojja Vani, Balaji P, Gokulraj S, Srinivas P.V.V.S","doi":"10.53759/7669/jmc202404055","DOIUrl":"https://doi.org/10.53759/7669/jmc202404055","url":null,"abstract":"Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}