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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumar, S. Vinod Sunil, G. Hussein, Ramy Riad Manju Vidhya, S. Sundaram, S. Meenakshi Department of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Excel Engineering College Department of Artificial Intelligence and Data Science Komarapalayam India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798350360660

Facial Emotion Recognition (FER) plays a crucial role in analysing and understanding individual's emotions in the form of non-verbal communication. Existing Deep Learning (DL) based FER methods offered diverse contribution in analysing human emotions but several challenges like extracting accurate spatial and temporal features for accurate classification of emotions is a major concern. To address this, a DL based Facial Recognition and Emotion Detection (FRED) system is proposed by using extended Cohn-Kanade (CK+) dataset as the input. The data is pre-processed first where the Histogram Equalization (HE) is applied and then Convolutional Neural Network (CNN) is used to extract the spatial features while reducing the dimensionality. The extracted features are given to Recurrent Neural Network (RNN) to learn long term dependencies from a temporal sequence of data. The final classification is performed where the Attention Mechanism (AM) is integrated in the RNN to determine the higher weights for better outcome. The results of proposed AMConVnet-RNN showed a superior classification accuracy of 99.54%, precision of 98.63%, and f1-score of 97.39% when compared to existing FER methods like Residual Network-50 (ResNet-50) and Channel Based Attention Mechanism (CBAM) based ResNet50. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Electrocardiogram Analysis in Medicine:data,Methods,and Applications

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Chinese Medical sciences Journal 2023年第1期38卷 38-48页

作者： Yu-Xia Guan Ying An Feng-Yi Guo Wei-Bai Pan Jian-Xin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词： Electrocardiogram database preprocessing machine learning medical big data analysis

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ASCM: Analysis of a Sequential and Collaborative Model for Recommendations

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SN computer science 2024年第6期5卷 1-11页

作者： Tandon, Righa Verma, Ajay Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan 173 234 India Department of Data Science and Engineering School of Computing Mohan Babu University AP Tirupati 517 102 India

The recommender system can predict future lists of items based on the user’s sentiments and interactions. As the data is ubiquitous, we have a number of options available to make a selection. This is the scenario where the recommender system plays its role by narrowing the available options and making the selection process easy and effective. In this paper, we have proposed a sequential recommender system that considers the user interactions along with the items in sequential order and maps the sequences dynamically, in which the weight-age is given to current preferences and recent choices as explored by the user of the system. We have provided a detailed comparison of the proposed sequential recommender system with the commonly used matrix factorization model. The proposed recommender system is based on sequential dependencies and responds equally well to the cold start problem. We have obtained results using Movielens dataset, which reveal that the proposed recommender system is much faster with higher mean reciprocal rank (MRR) value. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering Machine learning Matrix factorization Recommender systems Sequential model Singular value decomposition

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Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches

Secured Provisioning of Multimedia Contents in Cloud Platfor...

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computer science and Emerging Technologies (CSET), International Conference on

作者： S.A. Sahaaya Arul Mary Akshaj Nevgi Rhea Ranjith Nair Jehan Sharukh Bhathena Angad Chaudhry S. Sharon Jessika School of Computer Science and Engineering VIT Computer Science and Engineering Computer Science with specialization in Data Science

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency.

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Extended Finite State Machine based Fault Tolerance in WSN 7

Extended Finite State Machine based Fault Tolerance in WSN

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Lekha, D. Ahsan Shariff, M. Lakshmi, S. Venkata Anwar Basha, H. Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Aalim Muhammed Salegh College of Engineering Tamil Nadu Chennai India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

ISBN: (纸本)9781665464086

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Finite automata

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Comparative analysis of alkali-treated natural fibres for improved interfacial adhesion in composite materials

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Interactions 2024年第1期245卷 1-13页

作者： Bhagat, Madhuri S. Jadhav, Varsha D. Kulkarni, Sumant Khanderao Satishkumar, P. Saminathan, Rajasekaran Department of Civil Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Natural fibers such as bamboo, kenaf, flax, ramie and hemp were chemically treated to improve their adherence with hydrophobic matrices. One common chemical process for altering the surface of these natural fibers is alkali treatment (AT). The experiment involves soaking bamboo fibers (BF) in solutions of 1, 10, and 30 wt % sodium hydroxide (NaOH) for one day, half an hour, and twenty minutes at room temperature. These alkali-treated and untreated bamboo fibers was compared with their physical and mechanical qualities. Using scanning electron microscopy (SEM) and the circular fiber assumption, the CSA of bamboo fibers both before and after AT were compared. Improving the AT procedures for BF at room temperature using varying NaOH concentrations is the focus of this effort. Additionally, the tensile characteristics of both untreated and variously treated AT Bamboo fibres are examined using a two-parameter Weibull distribution. Instead of just showing the average tensile strength, probabilistic tensile strength has shown to be a more effective method. This study proves without a reasonable doubt that adding 1 wt % NaOH to bamboo fiber makes natural fiber reinforced composites mechanically better. According to the SEM, the CSA of bamboo fibers decreases by about 13.62–20.24% and about 18.42–22.63%, respectively, after alkali treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Multi-head Hashing with Orthogonal Decomposition for Cross-modal Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Liu, Wei Li, Jun Wu, Zhijian Xu, Jianhua Yang, Bo School of Computer and Electronic Information Nanjing Normal University Nanjing210023 China School of Data Science and Engineering East China Normal University Shanghai200062 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Recently, cross-modal hashing has become a promising line of research in cross-modal retrieval. It not only takes advantage of complementary multiple heterogeneous data modalities for improved retrieval accuracy, but also enjoys reduced memory footprint and fast query speed due to efficient binary feature embedding. With the boom of deep learning, convolutional neural network (CNN) has become the de facto method for advanced cross-model hashing algorithm. Recent research demonstrates that dominant role of CNN is challenged by increasingly effective Transformer architectures due to their advantages of long-range modeling by relaxing local inductive bias. However, the absence of inductive bias shatters the inherent geometric structure, which inevitably leads to compromised neighborhood correlation. To alleviate this problem, in this paper, we propose a novel cross-modal hashing method termed Multi-head Hashing with Orthogonal Decomposition (MHOD) for cross-modal retrieval. More specifically, with the multi-modal Transformers used as the backbones, MHOD leverages orthogonal decomposition for decoupling local cues and global features, and further captures their intrinsic correlations through our designed multi-head hash layer. In this way, the global and local representations are simultaneously embedded into the resulting binary code, leading to a comprehensive and robust representation. Extensive experiments on popular cross-modal retrieval benchmarking datasets demonstrate the proposed MHOD method achieves advantageous performance against the other state-of-the-art cross-modal hashing approaches. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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An Accurate Stock Prediction Using Ensemble Deep Learning Model

An Accurate Stock Prediction Using Ensemble Deep Learning Mo...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Baswaraj, D. Rekha, Ravula Shashi Divya, V. Natchadalingam, R. Sahni, Nancy Reddy, Pundru Chandra Shaker Computer Science and Engineering Vasavi College of Engineering Telanagana Hyderabad India Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India CSE-Data Science R.L.Jalappa Institute of Technology Karnataka Bengaluru India Mittal School of Business Management Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305708

Financial textual, statistical, and graphical data have all seen increased interest in machine learning and deep learning as analytical tools in recent years. Anticipating the trend of a stock price in the future is challenging applications of deep-learning(DL) in economics. Several factors influence the amplitde &incidence of ascend and descend of stocks at the alike time, making it difficult to predict their future movement. In addition, these are merely company-level considerations;industry-performance, investor-mood, and economic-factors also have a role in determining the direction of equities in the future. In this research, we offer a unique deep learning method for forecasting stock price behavior in the future. The model combines two recurrent-neural-networks(RNN) with a fully connected NN using a mixing ensemble learning technique. The Standard & Poor's 500 Index serves as a case study for our investigation. Our blending ensemble-DL strategy significantly outperforms the best existing anticipation desgn on the same dataset by decreasing the mean-squared-error(MSE) by 58%, growing the precision by 41%, the recall by 52%, the F1-score by 45%, and the movement-direction-accuracy by 35%. The goal study is to detail our intend ethos and demonstrate how EDL may more correctly anticipate upcoming stock price patterns and help investors make sound financial decisions than more conventional approaches. © 2023 IEEE.

关键词： Deep-learning Ensemble-learning Statistical-finance Stock-prediction

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