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Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Intelligent Automation & Soft Computing 2023年第4期36卷 1027-1042页

作者： Swarna Sudha Muppudathi Valarmathi Krishnasamy Department of Computer Science and Engineering Ramco Institute of TechnologyRajapalayam626117India Department of Electronics and Communication Engineering P.S.R.Engineering CollegeSivakasi626140India

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of *** use of SNS often inﬂicts the physical and the mental health of the ***,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative inﬂ*** state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum *** overcome these issues,the proposed methodology utilizes a Modiﬁed Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectiﬁed Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its *** stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overﬁ*** LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation *** also doesn’t stop the learning process when the values are *** MCNN computes a speciﬁed score value using a novel integrated anomaly detection *** on the score value,the anomalies are identiﬁed.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modiﬁed CNN and fast convergence is *** this way,the performance of the model is enhanced in terms of classiﬁcation *** efﬁciency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,speciﬁcity,recall,and *** proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.

关键词： Anomaly detection convolutional neural network social networking sites stochastic pooling teacher learner-based optimization

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Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

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Play Store App Rating Prediction Using Machine Learning & Deep Learning Regression Techniques 2

Play Store App Rating Prediction Using Machine Learning & De...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Biswal, Bhramara Bar Das, Satya Narayan Sahu, Satyanarayan Banerjee, Shobhan Computer Science and Engineering Gandhi Institute of Engineering and Technology Gunupur India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350308846

The rating & review of an app in the App Store or Play Store plays an essential role for end users to get information about the app based on other people's experiences with that app. The reviews might be context-based, but the ratings give a firsthand impression to the users, simply looking at scores out of five. In this paper, we have performed multivariate analysis on the Google Play Analysis Dataset to determine the critical features and prune off the redundant ones, followed by which we compared the accuracy of various regression models in predicting the ratings of various apps in the dataset. © 2023 IEEE.

关键词： Artificial Neural Network Feature Importance Multivariate Analysis Rating prediction Regression

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Vitamin Deficiency Detection Using Neural Networks 9

Vitamin Deficiency Detection Using Neural Networks

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Krishna Chaithanya, T. Gupta, Jiniya Roobini, M.S. Department of Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350350845

Vitamin deficiency is a pervasive health issue affecting millions globally, often leading to severe health complications if undiagnosed. Various vitamin deficiencies can be identified by identifiable symptoms manifesting in numerous areas across the human body. Blood tests are most commonly used to detect deficiencies. However, blood tests are expensive and have limited availability. The project aims to develop an automated system to detect vitamin deficiencies using Mobilenet, convolutional neural networks (CNN), NasNet-mobile, inception modules, and artificial neural networks (ANN). The program allows users to detect potential vitamin deficiencies without needing blood samples by examining images of the eyes, lips, tongue, and nails. Here we have considered the dataset of abnormalities caused by vitamin deficiencies like glaucoma eyes, bluish nails, and other abnormalities of lips and tongue. We have trained the model on these image datasets, by leveraging image recognition and data analytics, the proposed system offers a quick, efficient, and non-invasive method of detection thereby streamlining the diagnostic process and facilitating early intervention. The aim is to provide an economical, accessible, and efficient clinical screening method. Moreover, the platform furnishes tailored dietary suggestions to tackle pinpointed inadequacies, thus alleviating health hazards linked with insufficient nourishment. By furnishing an easily reachable resource for prompt identification and action, our system aids in tackling a prevalent worldwide health concern. The program functions as a beneficial aid for individuals to combat a widespread global challenge, impacting millions due to inadequate comprehension of nutrition. Serving as a valuable resource, it addresses an international issue, assisting those facing nutritional uncertainties worldwide. © 2024 IEEE.

关键词： Blood

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Distance Matrix and Markov Chain Based Sensor Localization in WSN

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computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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Navigating the Landscape of Ransomware Detection Methods: A Review 3rd

Navigating the Landscape of Ransomware Detection Methods: A ...

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Benito, Nikiema Martial, Ouedraogo Banda, Tamiano Sahana, Subrata Department of Computer Science and Engineering School of Engineering and Technology Sharda University UP Greater Noida201306 India

ISBN: (纸本)9789819719600

The global impact of ransomware on cybersecurity has increased alarmingly in recent years. It is the cause of important financial damage for individuals as well as for corporations. From the early days of computers, this type of malware has never ceased to evolve, finding new ways to evade current anti-ransomware detection. Ransomware is a malicious code or piece of code designed to extort payment from victims. Considerable advances have been done on ransomware detection. Nevertheless, many of the proposed solutions fall short because of the continuous sophistication of ransomware. This paper provides a comprehensive review of the recent evolution of ransomware and a critical analysis of state-of-art research contributions based on well-defined evaluation metrics. Our analysis covers various ransomware detection techniques and contrasts their merits and limitations. This review goal is to be a constructive guide for ransomware-oriented research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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CHIE: Generative MRC Evaluation for in-context QA with Correctness, Helpfulness, Irrelevancy, and Extraneousness Aspects 2

CHIE: Generative MRC Evaluation for in-context QA with Corre...

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2nd Workshop on Generalisation (Benchmarking) in NLP, GenBench 2024

作者： Phatthiyaphaibun, Wannaphong Nonesung, Surapon Limkonchotiwat, Peerat Udomcharoenchaikit, Can Sawatphol, Jitkapat Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand

ISBN: (纸本)9798891761827

The evaluation of generative models in Machine Reading Comprehension (MRC) presents distinct difficulties, as traditional metrics like BLEU, ROUGE, METEOR, Exact Match, and F1 score often struggle to capture the nuanced and diverse responses. While embedding-based metrics such as BERTScore and BARTScore focus on semantic similarity, they still fail to fully address aspects such as recognizing additional helpful information and rewarding contextual faithfulness. Recent advances in large language model (LLM) based metrics offer more fine-grained evaluations, but challenges such as score clustering remain. This paper introduces a multi-aspect evaluation framework, CHIE, incorporating aspects of Correctness, Helpfulness, Irrelevance, and Extraneousness. Our approach, which uses binary categorical values rather than continuous rating scales, aligns well with human judgments, indicating its potential as a comprehensive and effective evaluation method. © 2024 Association for Computational Linguistics.

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Sleep Stage and Sleep Disorder Detection using Spatial and Temporal Attention based Capsule Network 1

Sleep Stage and Sleep Disorder Detection using Spatial and T...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Devi, R. Renuga Elavarasi, S. Anitha Venkateswarlu, Chetti Anitha, R. Ali, Hassan Mohamed Srm Institute of Science and Technology Ramapuram Department of Computer Science and Applications Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Maisammaguda Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350352931

Sleep is referred as a cyclic process which is the most important part in human life. The sleep disorder and its stages are observed from time waveforms such as Electroencephalogram (EEG), Electrocardiograms (ECG), and Electromyograms (EMG) while diagnosing patients. However, the existing Deep Learning (DL) based neural network used for multi-modal sleep stage and sleep disorder detection has limitations in decision making that results in inaccurate detection of sleep stage. To overcome this limitation, a Spatial and Temporal Attention based Capsule Network (STA-CapsNet) is proposed for sleep stage and sleep disorder detecting for multimodal waveforms. The proposed CapsNet model with spatial and temporal attention mechanism focuses on informative regions that helps in making decision and classifies the sleep stages accurately. Firstly, the time waveforms are acquired and preprocessed to convert the signals into Red Green Blue (RGB) images for precise classification of sleep disorders. These RGB images are fed to proposed STA-CapsNet model for classifying the six stages of sleep correctly. Experimental results of the proposed STA-CapsNet method achieved accuracy of 9 6. 8 9 %which is higher than the existing approaches such as Convolutional Neural Network (CNN) and Visual Geometry Group-16 (VGG-16). © 2024 IEEE.

关键词： Sleep research

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Fast and Accurate Community Detection in Social Media Networks with a Multi-Algorithmic Approach

Fast and Accurate Community Detection in Social Media Networ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Pabitha, P. Petchiammal, G. Eswari Anna University Department of Computer Technology MIT Campu Chromepet600044 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem637504 India

ISBN: (纸本)9798331543617

Community detection is an essential technique for uncovering structural patterns in complex networks, such as social, biological, and information networks. In this paper to explore a hybrid approach to community detection by combining the label propagation algorithm and leiden algorithm. Although LPA is well known for its computational effectiveness in large- scale networks, because of its reliance on local label propagation, it frequently results in unstable and fractured communities. High- quality community structures are produced by the Leiden algorithm, which is more computationally expensive than the Louvain method but ensures well-connected communities by optimising modularity. Our goal in combining these two approaches is to take use of LPA's speed for quick community discovery and the Leiden algorithm's refinement powers for increased accuracy and stability. Large or dynamic networks where both real-time speed and community quality are crucial are best suited for the suggested hybrid method. The combination of these algorithms yields more cohesive and modular communities than either one working alone, as evidenced by experimental results on synthetic social network data. The method is a useful tool for large- scale network analysis because it strikes a balance between the necessity for strong and significant community structures and computational efficiency. © 2024 IEEE.

关键词： Community detection Label propagation leidens Louvain

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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