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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Cybersecurity Threat Detection using Machine Learning on Network Traffic

Cybersecurity Threat Detection using Machine Learning on Net...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： R. Rithi N. Nithya A. Akila R. Saravanakumar M. Vargheese S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Web phishing is a prevalent online attack in which victims are tricked into revealing their personal information by visiting fraudulent websites. Phishers often use URL shortening tools to disguise malicious links, making it easier to steal sensitive data such as usernames, passwords, and banking details. These phishing websites are designed to mimic legitimate ones, making detection more challenging. As phishing techniques continue to evolve rapidly alongside technological advancements, the need for effective anti-phishing systems has become increasingly crucial. Machine learning has emerged as a powerful tool for recognizing and mitigating phishing scams. This article provides a comprehensive overview of machine learning techniques for phishing detection and their effectiveness in combating online threats.

关键词： Uniform resource locators Accuracy Reviews Phishing Scalability Machine learning Telecommunication traffic Passwords Threat assessment Real-time systems

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading

The Impact of Transformer Models and Regression Algorithms o...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Abhijith. N. Nampoothiri M. G. Thushara Pradeesh. N Anand Krishna K. R Abhai Santhosh Abhijith Kumar S Suryan Kumar Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Chennai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models—including BERT, Sentence-BERT, T5, Genism, and XLNet—are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations.

关键词： Support vector machines Measurement Accuracy Education Forestry Transforms Transformers Numerical models Reliability Random forests

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A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection

A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intellig...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Khadija Danladi Sankara G. Saritha S. Anitha Elavarasi S. Saradha D. Arul Kumar Masters of Software Engineering Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM's ability to capture temporal dependencies in sequential data and GAN's strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications.

关键词： Adaptation models Accuracy Predictive models Generative adversarial networks Data models Hybrid power systems Cyber threat intelligence computer security Long short term memory Anomaly detection

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LECF: recommendation via learnable edge collaborative filtering

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Science China(Information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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Smart Investigations into the Development of an Effective computer-Assisted Diagnosis System for CT Scan Brain Depictions

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SN computer Science 2023年第5期4卷 1-11页

作者： Attuluri, Sasidhar Bhupati, Ch Ramya, Lavu Tiwari, Amit Budaraju, Raja Rao Cotrina-Aliaga, Juan Carlos Software Developer Applications 671 E Royal Ln APT 1074 Irving 75039 TX United States Department of IoT K L Deemed to be University Vaddeswaram Guntur 522302 India Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation Chinna Avutapalli Andhra Pradesh Vijayawada 521286 India Department of Computer Applications ABES Engineering College Ghaziabad India Oracle 3990 Scottfield Street Dublin 94568 CA United States Faculty of Human Medicine Universidad Privada San Juan Bautista Chincha Peru

Recent advances in medical image analysis on computers are anticipated to help radiologists and other healthcare workers with numerous diagnostic tasks including medical image interpretation. Accurate diagnosis and/or assessment of a condition in medical imaging relies on both the quality of the acquired images and the quality of the interpretation of those images. To accomplish this, large amounts of picture data and medical records must be combined. computer-aided diagnosis (CAD) systems have been developed in response to a lack of accuracy to boost the radiologist's productivity and precision in their interpretations. Given the importance of computerised tomography (CT) imaging in this field, we have made an effort in our research to examine CT scan brain images by applying a number of feature extraction and selection techniques, as well as classification techniques, to diagnose different types of brain disorders. Brain CT scans are analysed here and classified as either normal, benign tumours, or malignant tumours. Finding the best characteristics to use in a classification system is called “feature selection,” and it requires sifting through a vast amount of extracted features to locate the most relevant ones. To evaluate the efficacy of the implemented classifiers, we used measures of accuracy, specificity, sensitivity, positive prediction value, and negative prediction value. Traditional classifiers are also examined alongside the suggested method's results. The proposed decision support systems outperform the standard classifiers in terms of accuracy. An FSVM is generated by applying the RBF kernel function to this dataset. This method is compared to the support vector machine (SVM) and the multi-layer perceptron neural network (MLPNN) in terms of accuracy, sensitivity, and specificity of the classifiers they produce. Accuracy (96.25%), sensitivity (96.67%), and specificity (95.83%) are all significantly higher when using the proposed method as compared to t

关键词： CAD CT Fuzzy SVM NPV PPV SVM

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Machine Learning on Retail Stores for Customer Churn

Machine Learning on Retail Stores for Customer Churn

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Advanced Computing Technologies (ICoACT), International Conference on

作者： P. Brito Clinton P. Sathiya Priya S. Jagadeesan S. Nagarajan K. Mohanapriya S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The pace at which customers discontinue engaging in commercial activity with a corporation is known as customer churn, and it frequently indicates discontent or competition. Machine learning analyzes customer data to identify patterns, predict churn, and personalize retention strategies. It enables businesses to target at-risk customers and enhance engagement through tailored interventions and offers. A public dataset gathered from Kaggle is used in this research work. Exploratory data analysis is carried over to understand the patterns and trends in the churn rate. The bivariate analysis is held to find the correlation of each feature against the churn rate. With these observations, the importance of marketing strategies is exhibited. Additionally, four distinct models—Logistic Regression, Random Forest, Decision Tree, and the XGB Classifier-are used to train and assess the dataset. From the evaluation of all four models with the help of the ROC score, the XGB is found to outperform and provide better results obtaining 98% accuracy and a 1.00 ROC score.

关键词： computers Data analysis Correlation Computational modeling Market research Classification algorithms Churn Regression tree analysis Random forests Optimization

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Neural Networks and Deep Learning: Innovations and applications in Soft Computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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Knowledge Graph Completion Method Based on Using Multi-Hop Reasoning

Knowledge Graph Completion Method Based on Using Multi-Hop R...

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Aleksei Golovin Nataly Zhukova Igor Kulikov Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University ‘LETI’ Saint-Petersburg Russia St. Petersburg Federal Research Centre of the Russian Academy of Sciences (SPCRAS) Saint-Petersburg Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data. Within the research, the method for restoring the structure of a knowledge graph using sequence-to-sequence multi-hop reasoning, based on the transformer architecture, is proposed. To evaluate the effectiveness of the proposed method, the study of its properties was carried out and the results were compared with the results of the existing methods used for predicting relations in the knowledge graphs. The proposed solution almost achieves the same results as methods based on embeddings. The evidential paths generated by the proposed method ensure the interpretability of the results. Our method does not rely on existing relations to create an evidential path and has the flexibility to restore missing relations in the path.

关键词： Knowledge graphs Reinforcement learning Transformers Cognition Information technology Nanotechnology Convergence

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques

Weapon Detection Identification and Classification for DCNN ...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： J. Ashok Kumar G. Dhivya P. Selvaraj V. Ganesh Karthikeyan S. P. Ramesh S. Jeya Lakshmi R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing SASTRA Deemed to be University Thirumalaisamudram Thanjavur Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification.

关键词： YOLO Accuracy Convolution Weapons Neural networks Feature extraction Real-time systems Object recognition Web sites Videos

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