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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Caroline Mary, A. Senthil Kumar, A.V. Saleh, Omar S. Research Scholar Department of Computer Science Hindusthan College of Arts and Science Coimbatore India Assistant Professor Department of Applied Mathematics and Computational Sciences PSGCollege of Technology Coimbatore India Professor and Director Department of MCA Hindusthan College of Arts and Science Coimbatore India Ministry of Higher Education and Scientific Research Baghdad Iraq

ISBN: (纸本)9789819940707

Underwater communications are playing a major role in recent times as underwater happenings have gained interest. Especially in scenarios based on natural calamities like tsunamis, pollution detection, and surveillance, underwater communications are indispensable. Parameters like battery life span and capabilities of sensor nodes play a vital role in the underwater environment. QoS has to be taken care of in underwater communication. Congestion avoidance helps in using energy efficiently in underwater communication. Simulation results prove that this algorithm overcomes the drawback of a normal backpressure-based, static priority algorithm where packet loss, delay, the energy of nodes, and routing overhead are reduced overall, and packet delivery ratio and throughput are increased in special cases of real-time important packets. As underwater nodes are battery-operated, it is essential to have energy-efficient scheduling algorithm. The proposed algorithm solves the drawback of the backpressure algorithm by having energy-efficient queue length stabilizer (QLS) technique, where the backlog is adjusted such that no queues are affected and the energy consumption is less. And real-time packets are treated effectively such that real-time applications can be supported using this scheduling algorithm. The novelty of the proposed method is congestion control methodology with active priority and energy efficiency for an underwater network for effective scheduling of packets where queue backlog size is also adjusted virtually based on energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Scheduling algorithms

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Cyberbullying Detection Using Deep Learning: A Comparative Study 2

Cyberbullying Detection Using Deep Learning: A Comparative S...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alkasassbeh, Mouhammd Almomani, Ammar Aldweesh, Amjad Al-Qerem, Ahmad Alauthman, Mohammad Nahar, Khalid Mago, Beenu Princess Sumaya University For Technology Computer Science Department Amman Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan Yarmouk University Faculty Of I.T Computer Science Department Irbid Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350394962

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field. © 2024 IEEE.

关键词： Social networking (online)

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of computer science & technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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Enhance Social Network Bullying Detection using Multi-Teacher Knowledge Distillation with XGBoost Classifier

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IEEE Access 2025年 13卷 95618-95627页

作者： Prasomphan, Sathit King Mongkut’s University of Technology North Bangkok Department of Computer and Information Science Faculty of Applied Science 1518 Pracharat 1 Rd. Wongsawang Bangsue Bangkok10800 Thailand

Cyberbullying remains a pressing issue in Thai social media, especially among teenagers. While many studies have explored deep learning approaches for sentiment analysis or toxicity detection, the detection of cyberbullying—especially in the Thai language—remains underexplored. This study introduces a novel framework that enhances cyberbullying detection by integrating Multi-Teacher Knowledge Distillation (MTKD) with an XGBoost classifier, specifically adapted for Thai-language social media posts. Unlike prior work that relies solely on neural models, this research demonstrates how distilled soft labels from diverse teacher models can be effectively transferred to a lightweight and interpretable XGBoost student model. A key contribution of this study is the successful adaptation of XGBoost, traditionally used for structured/tabular data, for a natural language classification task by using rich semantic features extracted via pre-trained NLP models. Additionally, although the selected datasets (Wisesight, Thai Toxic Tweet, and 40 Thai Children Stories) are often used for sentiment analysis, we reframe and preprocess them for the purpose of cyberbullying classification by focusing on toxic, harmful, or aggressive linguistic patterns. Our framework achieved strong classification performance—92.5%, 90.5%, and 91.0% accuracy across the three datasets—demonstrating its robustness and practical application in Thai-language cyberbullying detection. © 2013 IEEE.

关键词： Teaching

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Momentum-dependent flow correlations in deformed nuclei at collision energies available at the BNL Relativistic Heavy Ion Collider

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Physical Review C 2023年第5期107卷 054916-054916页

作者： Rupam Samanta Piotr Bożek AGH University of Science and Technology Faculty of Physics and Applied Computer Science aleja Mickiewicza 30 30-059 Cracow Poland

Flow fluctuations in ultrarelativistic heavy-ion collisions can be probed by studying the momentum dependent correlations or the factorization-breaking coefficients between flow harmonics in separate kinematic bins (transverse momentum p or pseudorapidity η). We study such factorization-breaking coefficients for collisions of deformed U238+U238 nuclei to see the effect of the nuclear deformation on momentum dependent coefficients. We also study momentum dependent mixed-flow correlations for the isobar collision system: Ru96+Ru96 and Zr96+Zr96, which have the same mass number but different nuclear structure, thus providing the ideal scenario to study nuclear deformation effect on such observables. We use the TRENTO + MUSIC model for simulations and event-by-event analysis of those observables. We find that these momentum dependent correlation coefficients are not only excellent candidates to probe the fluctuation in heavy-ion collision, but also show significant sensitivity to the nuclear deformation.

关键词： Collective flow Particle correlations & fluctuations Relativistic heavy-ion collisions

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Unveiling the Power of Sparse Neural Networks for Feature Selection

arXiv

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arXiv 2024年

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. Our code and the supplementary material are available on GitHub (https://***/zahraatashgahi/Neuron-Attribution). © 2024, CC BY-SA.

关键词： Feature Selection

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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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computer Systems science & Engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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Rock vs Mine Prediction and Detection for Aquatic Systems: A Comparative Analysis of Different Machine Learning and Deep Learning Algorithms

Rock vs Mine Prediction and Detection for Aquatic Systems: A...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sar, Ayan Choudhury, Tanupriya Goel, Anvi Kotecha, Ketan Pant, Bhasker Dewangan, Bhupesh Kumar School of Computer Science Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Symbiosis Institute of Technology Symbiosis International University Symbiosis Center for Applied Artificial Intelligence Maharashtra Pune411045 India Graphic Era Deemed to Be University Cse Dept Uttarakhand Dehradun248002 India Op Jindal University Department of Computer Science and Engineering Chhattisgarh Raigarh India

ISBN: (纸本)9798350373783

Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and efficiency in rock and mineral prediction. The researchers employed a series of watermarks that appeared and then utilized a convolutional neural network (CNN) to extract significant characteristics. Using transfer learning approaches and state-of-the-art optimization algorithms, researchers suggested a multi-class solution to identify between rocks and minerals. The suggested technique accurately predicted and recognized rocks and mines in underwater imagery, even in complicated settings with complicated against and illumination circumstances. The finding was essential for water protection and maritime safety systems, as it can assist in the automatic identification of rocks and minerals, decreasing the time and effort necessary for manual inspection © 2024 IEEE.

关键词： Convolutional neural networks

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms 4

Two-Area Power System with Automatic Generation Control Util...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sati, Madan Mohan Kumar, Dinesh Singh, Akhilesh Raparthi, Mohan Alghayadh, Faisal Yousef Soni, Mukesh Graphic Era Hill University Deptt. of Mathematics Bhimtal263136 India Srm Institute of Science and Technology Dept. of Computer Science and Engg. UP Ghaziabad201204 India Uttarakhand Technical University Campus Institute Deptt. of Electrical Engg. Pithoragarh India Alphabet Life Science Software Engineer DallasTX75063 United States Almaarefa Univ Riyadh College of Applied Sci Computer Science and It Department Saudi Arabia Dr. D. Y. Patil Vidyapeeth School of Science and Technology Pune India

ISBN: (纸本)9798350343670

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications. © 2024 IEEE.

关键词： and TD (Transmission Delay ANN BBBC FOPID GDB (Generation Demand Balance) GRC (Generation Rate Constraint) Load Frequency Control (LFC) PID PSO

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Evaluating Time Series Database Management Systems for Insurance Company 13

Evaluating Time Series Database Management Systems for Insur...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Koschel, Arne Astrova, Irina Alsleben, Stephan-Tobias Bellok, Jannis Meyer, Niklas Meyer, Sebastian University of Applied Sciences and Arts Hannover Faculty Iv Department of Computer Science Hannover Germany Tallinn University of Technology School of It Department of Software Science Tallinn Estonia

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

This paper provides the criteria and sources of information that an insurance company should consider when evaluating time series databases for its needs. The company wanted to select either QuestDB or Graphite in order to replace InfluxDB. Selecting a new database is an important process. Like any other successful database management system, the database selected today begins to define the legacy of the company's future. There are many different parameters that can be used by the company to evaluate the time series database alternatives, and there is no single most correct process for conducting this evaluation. Not only are there many possible pertinent evaluation criteria, but there also is typically a degree of uncertainty about the requirements and characteristics of the target application environment. This paper focuses on evaluation criteria, with emphasis on horizontal scalability and performance. © 2022 IEEE.

关键词： Database systems

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