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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kannappan, A. Kolagani, Nagesh Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The condition of the ocean is monitored through a network of acoustic sensor nodes. After the transmitter and receiver nodes are formed, they are placed in a shallow sea environment. Underwater communication takes more time, so optimization of it is a top priority. Cat swarm optimization method and sea lion optimization algorithms are two examples of currently used bio-inspired algorithms. By analyzing the output parameters of both algorithms, we can see which one is more optimized. When comparing cat swarm optimization techniques to, the sea lion algorithm provides superior energy efficiency by lowering the amount of power required to process acoustic sensor node data. Existing cat swarm optimization Algorithms compared with sea lion optimization algorithm. The amount of energy difference that occurs in the two algorithms is nearly up to 18 % more than energy efficiency. Sea lions and cat swarm based upon condition need the individual or group performance, these recreate the bio inspire algorithm nodes. © 2024 IEEE.

关键词： Underwater acoustic communication

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Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

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computer systems science & engineering 2023年第3期44卷 2147-2163页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

Android devices are popularly available in the commercial market at different price levels for various levels of *** Android stack is more vulnerable compared to other platforms because of its open-source *** are many android malware detection techniques available to exploit the source code andfind associated components during execution *** obtain a better result we create a hybrid technique merging static and dynamic *** this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing *** the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given *** Android Sensitive Permission is one major key point to be considered while detecting *** select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or *** goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

关键词： Zero-day exploit hybrid analysis principal component analysis supervised learning smart cities

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User-Induced Heuristics for Security-Constrained Unit Commitment: Variable Influence Diving and Variable Significance Neighborhood Search

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IEEE Transactions on Power systems 2025年第4期40卷 3334-3346页

作者： Li, Peijie Liao, Changtao Qi, Junjian Bai, Xiaoqing Wei, Hua Guangxi University School of Electrical Engineering Nanning530004 China South Dakota State University McComish Department of Electrical Engineering and Computer Science BrookingsSD57007 United States

The heuristics in mixed- integer linear programming (MILP) solvers are mostly general-purpose, which can only recognize some common structures. It sometimes may be challenging for them to obtain a high-quality feasible solution, thus reducing the overall efficiency of the MILP solver for solving large-scale security-constrained unit commitment (SCUC) problems. Based on the knowledge of the SCUC problem structure, this paper proposes two user-induced heuristics: variable influence diving (VID) and variable significance neighborhood search (VSNS). VID can iteratively select and fix fractional binary variables based on system parameters and the characteristics of SCUC, leading to the generation of a high-quality initial solution. In VSNS, a novel neighborhood-defining method is proposed, which identifies and fixes some principal binary variables by calculating a significance indicator. The proposed two heuristics can be built into any MILP solver and used for any node in branching, exploiting the input information after the presolve phase. The two heuristics are implemented in the CBC solver and tested on the IEEE 118- bus system, the Polish 2383- bus system, and the French system with 1888 buses, 1951 buses, and 2848 buses. The results demonstrate that the proposed approach can greatly enhance the computational efficiency for solving the SCUC problem. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection system 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

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Major Software Security Risks in Banking Industry: Design Phase Perspective 3

Major Software Security Risks in Banking Industry: Design Ph...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Srivastava, Archana Singh, Nilu Suhail, Kamran Ybn University Department of Computer Science and Engineering Ranchi India Klep University Department of Computer Science and Engineering India School of Management Sciences Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350336238

As the globe transitions to the internet age, software has emerged as the factor primarily essential to the success of the digital realm. Software now permeates every aspect of daily existence in the age of computers. It is relatively simple to administer software in any business for employees, employers, and other components of the organization. Our daily lives are made more convenient and hassle-free by this software. Shopping, news, processing sales data, architectural, medical, banking, and other aspects of our lives are all handled digitally. One of the most significant fields today is online banking, which also relies on software. Nevertheless, software security is also a significant concern. Any program, especially banking software, that has security flaws can cause us problems. The development of secure software becomes extremely difficult if there is a compromise at the design stage. Therefore, it must be avoided throughout the design phase. Software security risks are software flaws that allow harmful activities to occur accidentally. Consequently, it is a matter that must be properly managed during the design phase. The majority of Developers rely more on their knowledge and experience in risk management than on risk management frameworks. From the Common Weakness Enumeration (CWE), the authors have compiled a list of potential vulnerabilities that could be discovered during software design. © 2023 IEEE.

关键词： Risk management

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Signal Conducting system with Effective Optimization Using Deep Learning for Schizophrenia Classification

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computer systems science & engineering 2023年第5期45卷 1869-1886页

作者： V.Divya S.Sendil Kumar V.Gokula Krishnan Manoj Kumar School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia Department of Electrical and Electronics Engineering S A Engineering CollegeChennaiTamilnaduIndia Department of Computer Science and Information Technology CVR College of EngineeringMangalpallyHyderabadTelanganaIndia School of Computer Science University of Petroleum and Energy StudiesDehradunIndia

Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.

关键词： Deep learning optimization algorithm signal conducting system schizophrenia convolutional neural network mayfly optimization algorithm

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A Novel Hybrid Recommendation system for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques 4

A Novel Hybrid Recommendation System for Riyadh Restaurants:...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rohilla, Vinita Kaur, Mandeep Chakraborty, Sudeshna Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India Galgotia University Department of Computer Science and Technology Greater Noida India

ISBN: (纸本)9798350364729

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh. © 2024 IEEE.

关键词： Wiener filtering

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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