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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Maragatham, T. Vinodhini, B. Yuvarani, P. Samyugtha, K. Computer Technology Kongu Engineering CollegePerundurai Erode India Computer Science and Engineering Sns College of Technology Coimbatore India

ISBN: (纸本)9798350300093

Power distribution and transmission depend heavily on transformers. Transformers encounter a variety of problems, including low voltage power, power loss, power wastage, power theft, phase-to-phase faults, overloading, and others. The HI (also known as health index) tool is used to address power transformer issues. The method of calculating the power transformer's health index is challenging and time-consuming, and there is a greater probability of data uncertainty and missing data. With the help of the Gradient boost decision tree(GBDT) method, this research intends to increase the accuracy of determining the health status of the power transformer. GBDT is a machine learning method for improving the prediction power of the model across successive learning stages. The GBDT is compared with Random forest (RF) and the accuracy provided by GBDT is 97.5%. © 2023 IEEE.

关键词： Decision trees

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Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

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computer Systems science & engineering 2022年第3期40卷 963-978页

作者： G.Maria Jones S.Godfrey Winster P.Valarmathie Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Saveetha Engineering CollegeChennai602105India

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among *** paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics *** describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying *** use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the *** on a real-time dataset reveal better results for the detection of cyberbullying *** Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE *** LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

关键词： Mobile forensics cyberbullying machine learning investigation model suspicious pattern

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Hybrid Artificial Neural Network with Advanced Noctiluca Optimization for Activity Monitoring and Prognostic Study of Signals Using EEG Signals

Hybrid Artificial Neural Network with Advanced Noctiluca Opt...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Kalaivani, K. Kala, R. Arun, R. Brindha, B. Thirumangaialwar, E. Babu Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Rvs College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350334050

Before earlier, the primary reason why academics were interested in human observations from books was so they could quickly identify human cognition. Conversely, these signals from linguistic processing can also help with machine learning (ML) algorithms. The potential of using EEG cognitive function for this function is mainly untested at this time. This paper presents the first comprehensive assessment of the capability of EEG brainwave activity data for monitoring and predicting emotional analysis by analysis of recorded Electroencephalography (EEG) signals using a novel Optimized Artificial Neural network (OANN) approach. With ANN and ANO working together, we can pick the best possible values for weights. To further improve activity prediction, the proposed method imports EEG data from the Bern-Barcelona EEG collection. With an accuracy rate of 97% for OANN for emotion recognition challenges, experimental evidence confirms the efficacy of these networks in terms of effectiveness metrics. © 2023 IEEE.

关键词： Electroencephalography

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Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection 18

Align before Attend: Aligning Visual and Textual Features fo...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Hossain, Eftekhar Sharif, Omar Hoque, Mohammed Moshiul Preum, Sarah M. Department of Computer Science and Engineering Department of Computer Science Dartmouth College United States Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Bangladesh

ISBN: (纸本)9798891760905

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach’s effectiveness with F1-scores of 69.7% and 70.3% for the MUTE and MultiOFF datasets. The scores show approximately 2.5% and 3.2% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://***/eftekhar-hossain/Bengali-Hateful-Memes. © 2024 Association for Computational Linguistics.

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An Important and Successful Multi-image Stereography-Based Secret Communication System for Military Applications 12th

An Important and Successful Multi-image Stereography-Based S...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ravi, C.N. Suhasini, T.S. Narasimharao, Jonnadula Mahendar, A. Amruthraj, B. Venkateswara Reddy, R. Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

A significant factor in data transfer is computer science. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-based underground messages and conceal in sequence. These days, there are numerous methods for concealing information on any media. A technique like this is steganography. The creation of a covert communication system that uses multi-image steganography will enable safe communication among the dispatcher and the recipient free from hacker intervention. Image steganography is the main technique for information hiding. It involves integrating the cipher text within a picture called the cover picture that is very difficult for strangers to view through an unassisted eye. Within a cover image, the concealed data may take the shape of any kind of text, picture, music, or even video. The secret code is split up into several pieces and etched into several cover images, according to the conceptual notion of multi-image steganography. Thus, in order to make it extremely difficult for the hackers to hide the data, we suggested two image steganography concepts. In order to create a safe and protected system, this learning suggest by means of the Advanced Encryption Standard (AES) approach of cryptography with the Least Significant Bit (LSB) method of steganography. Here, a symmetric key—which is used by both the sender and the recipient to encrypt and decode data—is used by both parties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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Internet of Things Assisted Human Detection and Rescue Robot: A Game Changer in the Disaster Environment 7

Internet of Things Assisted Human Detection and Rescue Robot...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Akhtar, Md Jawed Singh, Manmohan Suneel, Sajja Sutar, Anand Nayaki, A. Periya Khan, Nikhat Raza Ies College of Technology Department of Computer Science & Engg Bhopal India Ies College of Technology Department of Computer Science And Engineering Mp Bhopal India Hyderabad Telangana Dundigal500043 India Bharati Vidyapeeh College of Engineering Dept of Electronics And Telecommunication Navi Mumbai India Velammal College of Engineering And Technology Department of Information Technology Tamilnadu Madurai India Ies College of Technology Department of Computer Science And Engineering Bhopal India

ISBN: (纸本)9798350359299

In the realm of disaster response, the integration of Internet of Things (IoT) technologies has paved the way for innovative solutions to enhance human detection and rescue operations. This research work introduces a revolutionary approach through the development of the Challenging Human Detection and Rescue Robot (CHDRR). This robot incorporates advanced sensors, including the MQ6 Gas Sensor, LM335 Temperature Sensor, Humidity Sensor, and a Passive Infrared (PIR) Human Body Detection sensor, collectively forming a comprehensive sensory suite. The CHDRR leverages the power of IoT to enable real-time data collection, analysis, and communication, transforming disaster response capabilities. The MQ6 Gas Sensor ensures the detection of hazardous gases in the environment, providing crucial information for safe rescue operations. The LM335 Temperature Sensor and Humidity Sensor contribute to environmental monitoring, allowing the robot to adapt its strategies based on prevailing conditions. The PIR Human Body Detection sensor serves as a key element in the CHDRR's human detection capabilities. Through sophisticated algorithms and machine learning, the robot can identify and locate human presence amidst challenging disaster scenarios. The fusion of these sensors with IoT technology empowers the CHDRR to make informed decisions autonomously, optimizing its efficiency in dynamically changing environments. © 2024 IEEE.

关键词： Internet of things

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Machine learning-based opinion extraction approach from movie reviews for sentiment analysis

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Multimedia Tools and Applications 2025年第17期84卷 18599-18624页

作者： Jassim, Mustafa Abdalrassual Abd, Dhafar Hamed Omri, Mohamed Nazih MARS Research Laboratory University of Sousse Sousse Tunisia College of Engineering Al-Muthanna University Samawah Iraq College of Computer Science and Information Technology University of Anbar Ramadi Iraq

The field of sentiment mining, also known as sentiment analysis and sentiment extraction, has shown a surge with diverse applications. Various approaches to automate the process of sentiment analysis have been introduced focusing on machine learning, data mining, and natural language processing. Existing works examine the feelings embedded in people’s opinions and beliefs that affect various areas applications, including companies’ services and products. Movie review must undergoes various processes to detect and name feelings and achieve better accuracy in decision making. Due to the structure of the language, the difficulties have been increased for the natural language processing algorithms, particularly for the grammar and dictionary management. In this paper, a new approach for extracting words from a specific text and classifying them into positive or negative opinion classes is proposed. We employ pre-processing and extraction technique for the selection of 500 to 20,000 words based on their frequency, which can be utilized using vector construction. This is performed using "Term Frequency" and "Term Frequency-Inverse Document Frequency". Four Naive Bayes models (including Complement, Multinomial, Bernoulli, and Gaussian) are implemented and evaluated over unseen samples. Our extensive simulation results indicated that the Naive Bayes multinomial model achieved the best performance with 86.46% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Experimental investigation on machining of titanium composite using artificial neural network

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Interactions 2025年第1期246卷 1-10页

作者： Perumal, B. Kannan, R. Arunkumar, R. Kumaresan, T. Subbiah, Ram Department of Mechanical Engineering Sri Venkateshwara College of Engineering Andhra Pradesh Tirupati India Department of Mechanical Engineering PSNA College of Engineering and Technology Dindigul India Department of Mechanical Engineering MRK Institute of Technology Tamilnadu Kattumannarkoil India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Mechanical Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining process. This paper assesses the predictive capabilities of the Artificial Neural Network (ANN) and the response surface methodology in the titanium machining process with respect to tool tip temperature and tool wear. The adequacy of the ANN in modeling and forecasting reactions were rigorously examined and contrasted using data collected from properly prepared machining experiments. Both approaches performed admirably in terms of forecasting machining process responses. The coefficient of correlation (R2) obtained from the analysis verifies the ANN’s choice, with a maximum value of 99.9% and with the response surface methodology with a maximum value of 99.8% was found. The experiment also demonstrates that when the proper parameters are used, the ANN approach can produce the best results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Machining centers

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Combining Fuzzy Logic and Reinforcement Learning for Resource Management in Edge Computing 5

Combining Fuzzy Logic and Reinforcement Learning for Resourc...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Prasath, S. Ram Selvi, M. Krishnan, R. Santhana Nithila, Ezhil E. Malar, P. Stella Rose Sankar, G. Ram Scad College of Engineering and Technology Computer Science and Engineering Cheranmahadevi India Rajarajeswari College of Engineering Computer Science and Engineering Bangalore India Scad College of Engineering and Technology Electronics and Communication Engineering Chranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering Chennai Avadi India J.P. College of Engineering Electronics and Communication Engineering Thenkasi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350379945

With the wide spread of Internet of Things (loT) devices, edge computing plays a decisive part in meeting the computational demands of loT applications. Efficient resource allocation in such networks is essential to ensure optimal utilization of resources and meet the quality of service (QoS) necessities of loT applications. This research addresses the challenge of dynamic resource allocation in edge computing networks for loT applications through a novel hybrid AI approach, 'FledegeRL.' By integrating fuzzy logic and reinforcement learning, FledegeRL aims to optimize resource utilization and enhance system performance. The proposed framework is evaluated through simulation analysis and compared with existing algorithms, including Q-Iearning, Fuzzy Logic Control, and Ant Colony Optimization, using metrics such as resource allocation accuracy, average throughput, and network overhead. The results demonstrate that FledegeRL consistently outperforms these algorithms, achieving higher accuracy, greater throughput, and lower network overhead. This highlights the potential of FledegeRL as an effective solution for dynamic resource allocation in edge computing environments. © 2024 IEEE.

关键词： Quality of service

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