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

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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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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An Overview of Artificial Intelligence Diagnostic systems for Hepatitis B Virus 4

An Overview of Artificial Intelligence Diagnostic Systems fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Singh, Dalwinder Kaur, Harjeet School of Computer Science and Engineering Lovely Professional University Punjab India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350387490

A virus that can attack a human's liver and cause serious liver damage is primarily known as Hepatitis B Virus or HVB. It influences nearly all functionalities performed by the liver. It is basically a short term infection;however, it will become a chronic disorder and remains for a long term in some individuals. The severe effect of this illness is that it can cause chronic liver diseases, and with time it results in cancer of the liver. Hence, it puts a great risk to the life of a patient. The early detection of the virus is necessary, which assists in saving the patient's life and brings more treatment procedures that can adapt by the doctor for treatment. However, this life-threatening disease's therapy and diagnosis are both costly and can have dangerous adverse effects. As a result, developing a diagnostic system that lowers the cost of therapy and diagnosis is critical. In the medical field, several artificial intelligence technologies have been employed to assist doctors and experts in the diagnosing process. The significant aim of this paper is to thoroughly review all these diagnostic systems developed or proposed by various researchers. Later on, the systems are compared with one another to evaluate which system or model is best to use in solving the problem. The parameter that is taken into account to complete the comparison. As a result, it is noticed that the system developed by using a genetic neural network is accurate for the diagnosis of HBV and has an accuracy of 99.14%. © 2024 IEEE.

关键词： Artificial Intelligence Comparative Study Diagnostic systems Early Detection Hepatitis B Virus Intelligent systems Medical Expert systems

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Double Auction Strategies for Peer-to-Peer Energy Trading in Smart Grids: A Game Theoretic and Blockchain Approach 6

Double Auction Strategies for Peer-to-Peer Energy Trading in...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Naik, Madhusudan Pradhan, Nihar Ranjan Pratap Singh, Akhilendra National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350379297

The concept of using blockchain technology for peer-to-peer energy trading (P2PET) in smart grids, particularly employing a double auction-based game theoretic method, is a fascinating application of both blockchain and economic principles. This paper deals with a blockchain-enabled framework for P2PET in a smart grid by using a double auction-based game theory approach. The proposed permissioned blockchain network was developed using Hyperledger Fabric, Minifab and designed by configuring the *** file. Three smart contracts, or Chaincode namely Double Auction Order, CreateP2PEnergy, and Matching, have been written using Golang and deployed into the blockchain network, thereby creating automatic energy trading transactions between energy producers and consumers. The client communication, such as consumer queries, user input, and producer trading concepts, was designed using the Postman API. Additionally, the double auction framework's performance has been measured by Hyperledger Caliper. Analysis shows that when 1000 transactions were initiated in each round by clients using Hyperledger Caliper and REST API services, the Proof of Elapsed Time (PoET) mechanism successfully processed 946 transactions, in contrast to the 627 transactions managed by Proof of Work (PoW). The outcomes from multiple rounds of testing underscore the enhanced performance of this framework. In summary, the combination of blockchain technology, P2P energy trading, smart grids, and game theory offers a promising approach to revolutionizing the energy sector. By decentralizing energy trading, empowering consumers, and leveraging market mechanisms, this approach has the potential to enhance efficiency, promote renewable energy adoption, and foster a more sustainable energy ecosystem. © 2024 IEEE.

关键词： Blockchain

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Energy-Efficient Underwater Communication Using Bio-Inspired Algorithms in Metaheuristic Optimization 8

Energy-Efficient Underwater Communication Using Bio-Inspired...

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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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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering 24

Dual-Optimized Adaptive Graph Reconstruction for Multi-View ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wen, Zichen Wu, Tianyi Ren, Yazhou Ling, Yawen Cui, Chenhang Pu, Xiaorong He, Lifang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9798400706868

Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundance of graph data, the significance of multi-view graph clustering (MVGC) has become evident. Most existing methods focus on graph neural networks (GNNs) to extract information from both graph structure and feature data to learn distinguishable node representations. However, traditional GNNs are designed with the assumption of homophilous graphs, making them unsuitable for widely prevalent heterophilous graphs. Several techniques have been introduced to enhance GNNs for heterophilous graphs. While these methods partially mitigate the heterophilous graph issue, they often neglect the advantages of traditional GNNs, such as their simplicity, interpretability, and efficiency. In this paper, we propose a novel multi-view graph clustering method based on dual-optimized adaptive graph reconstruction, named DOAGC. It mainly aims to reconstruct the graph structure adapted to traditional GNNs to deal with heterophilous graph issues while maintaining the advantages of traditional GNNs. Specifically, we first develop an adaptive graph reconstruction mechanism that accounts for node correlation and original structural information. To further optimize the reconstruction graph, we design a dual optimization strategy and demonstrate the feasibility of our optimization strategy through mutual information theory. Numerous experiments demonstrate that DOAGC effectively mitigates the heterophilous graph problem. © 2024 ACM.

关键词： Graph neural networks

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Multiobjective Optimization for Traveling Salesman Problem: A Deep Reinforcement Learning Algorithm via Transfer Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 896-908页

作者： Gao, Le-Yang Wang, Rui Jia, Zhao-Hong Liu, Chuang Anhui University School of Computer Science and Technology Hefei230039 China National University of Defense Technology College of System Engineering Changsha410073 China Xiangjiang Laboratory Changsha410205 China Anhui University School of Internet Hefei230039 China

A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to address MOTSP. However, due to involving iteratively searching large solution space, they often entail significant computation time. Recently, deep reinforcement learning (DRL) algorithms have been employed in generating approximate optimal solutions to the single objective traveling salesman problems, as well as MOTSPs. This study proposes a multiobjective optimization algorithm based on DRL, called multiobjective pointer network (MOPN), where the input structure of the pointer network is redesigned to be applied to MOTSP. Furthermore, a training strategy utilizing a representative model and transfer learning is introduced to enhance the performance of MOPN. The proposed MOPN is insensitive to problem scale, meaning that a trained MOPN can address MOTSPs with different scales. Compared to meta-heuristics, MOPN takes much less time on forward propagation to obtain the pareto front. To verify the performance of our model, extensive experiments are conducted on three different MOTSPs to compare the MOPN with two state-of-the-art DRL models and two multiobjective meta-heuristics. Experimental results demonstrate that the proposed MOPN obtains the best solution with the least training time among all the compared DRL methods. © 2024 IEEE.

关键词： Traveling salesman problem

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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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Personalized Shatavari Nutrition Recommendations for Menopausal Women using Machine Learning Algorithms 5

Personalized Shatavari Nutrition Recommendations for Menopau...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Logapriya, E. Surendran, R. Soniya, M. Sathish Kumar, P.J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India R M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331504403

This research provides a comprehensive approach in designing a recommendation system for women's health issues, with emphasis on menopause and optimal eating plan with Shatavari nutrition plan incorporated. The data includes symptom severity;heart rate;temperature;sleep;and food and water intake, which has been obtained from patients' medical records, structured surveys, the users themselves, and through healthcare apps. Handling of missing values involves the completion of the missing records by forward method and imputation. Kindly find below the steps taken to pre-process the given data set for its numerical and categorical features respectively;Standard Scaler for numerical data set and OneHotEncoder for the binary data set. Variable creation is used to improve the performances of the models by creating new variables from other variables. Raw, selected and vectorized features are used to perform and compare multiple machine learning algorithms such as linear regression, logistic regression, support vector machines, decision random forest and gradient boosting, XGBoost, LightGBM, and CatBoost. The present study reveals that LightGBM has the maximum accuracy of 96%. The proposed system combines collaborative and content-based filtering, which will provide accurate and individual health and lifestyle recommendations for enhancing the quality of women's life during menopausal transition. © 2024 IEEE.

关键词： Support vector regression

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