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Multi-objective load balancing based on adaptive osprey optimization algorithm

International Journal of Information technology (Singapore) 2024年第6期16卷 3871-3878页

作者： Panneerselvam, Karthick Nayudu, P. Prathap Banu, M. Sheerin Rekha, P.M. School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyal India Department of Information Technology R.M.K. Engineering College Tamil Nadu Kavaraipettai India Department of Information Science and Engineering JSS Academy of Technical Education Karnataka Bangalore India

With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load balancing approach based on adaptive osprey optimization algorithm (AO²) within computing environments is proposed. Our proposed method aims to simultaneously optimize multiple objectives, such as minimizing energy consumption, cost and time, through the application of advanced optimization algorithms. The proposed system initially predicts the load of each virtual machine (VMs). After the prediction process, the tasks are assigned to the VMs. The research involves a comprehensive evaluation comparing the proposed approach with existing load balancing techniques, showcasing its effectiveness in achieving superior results. The findings demonstrate the potential of the multi-objective optimization algorithm to enhance load balancing efficiency in diverse computing scenarios, providing a valuable contribution to the field of distributed systems and resource management. © Bharati vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive osprey optimization algorithm Load balancing Multi-objective and energy consumption Virtual machine

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Synergizing Coati Optimization and Hybrid Neural Architecture for Optimized Psychological Disease Prediction 2

Synergizing Coati Optimization and Hybrid Neural Architectur...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mekala, M. Reshma, N. Chinnaraj, Aparna Karthika, R. Sundareswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331534950

Predicting psychological diseases using machine learning has gained prominence in healthcare informatics. This research introduces an Optimized Enhanced Hybrid Approach (OEHA) by integrating the Coati Optimization Algorithm with the previously proposed Hybrid Hopfield Recurrent Neural Network (H2RN2) model. This synergy aims to optimize feature selection in psychological disease prediction, ultimately leading to improved accuracy and efficiency. This research also proposes an enhanced hybrid approach that combines the power of Convolutional Long Short-Term Memory (ConvLSTM) with hybrid hopfield attention layer to predict psychological diseases through analysis of clinical Electronic Health Records (EHRs). This approach can handle the multimodal data by using ConvLSTM while addressing the challenges LSTM alone might struggle to effectively capture spatial information in clinical data. By using attention layer with hopfield network, variable-length sequences are handled, parallel processing is achieved, vanishing gradient problem is vanished, leading to accurate prediction. © 2024 IEEE.

关键词： Electronic health record

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Contagion Classification using Deep Learning Approach 2

Contagion Classification using Deep Learning Approach

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Kacham, Sai Prajwal Angayarkanni, S.A. Rohithshan, S. Mishra, Arpan Vignesh, K. SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communication Tamilnadu Kattankulathur603203 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

ISBN: (纸本)9798350365269

Potatoes are a major crop globally, but diseases can seriously harm their growth and quality. Recently, there's been a lot of interest in using machine learning to identify potato diseases from pictures. In this study, we've developed a way to automatically classify potato diseases using deep learning methods. By training a model with CNN and SVM algorithms on a dataset of potato disease images, we achieved an accuracy rate of 93.95%. This shows that deep learning is effective in identifying potato diseases. Our method could be a big help to farmers and researchers, making it faster and easier to spot and deal with potato diseases, which could lead to better crop yield and quality. © 2024 IEEE.

关键词： Support vector machines

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A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things

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Alexandria engineering Journal 2025年 127卷 619-627页

作者： Song, Wu Zhu, Xiangyuan Ren, Sheng Tan, Wenxue Peng, Yibo School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan Changde415000 China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Hunan Changsha410138 China

The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent traditional security mechanisms from addressing these threats. Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. Blockchain technology ensures data integrity, secures communication, and prevents unauthorised modifications through the proposed system. To reduce false positives and improve threat detection accuracy, XGBoost is able to reduce the number of false positives. Using the BOT-IoT dataset, the model is demonstrated to be superior to conventional intrusion detection systems. This approach ensures enhanced security and trustworthiness of IIoT networks by offering a scalable, efficient, and secure solution. © 2025

关键词： Industrial internet of things (IIoT)

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Assessment of ridge regression-based machine learning model for the prediction of automotive sales based on the customer requirements

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Interactions 2024年第1期245卷 1-13页

作者： Akash, C. Renga Vivekanandhan, P.K. Adam Khan, M. Ebenezer, G. Vinoth, K. Prithivirajan, J. Kishan, V. J. Pranesh Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Sales

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Bypassing LLM Safeguards: The In-Context Tense Attack Approach 14th

Bypassing LLM Safeguards: The In-Context Tense Attack Approa...

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14th International Conference on computer engineering and Networks, CENet 2024

作者： Wang, Shaohuang Geng, Ruijing Lei, Shuai Lv, Yanfei Zhang, Huaping School of computer science Xinjiang University Urumqi China Space Engineering University Beijing China Academy of Military Science Beijing China Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819642441

In the field of cybersecurity, the emergence of Large Language Models (LLMs) has opened up a new domain of potential risks. Although these models show an impressive level of capability across a range of applications, they are not free from the possibility of creating content that could negatively impact both social and digital safety. This paper examines the implications of In-Context Learning based command attacks, a burgeoning threat to the security and ethical integrity of LLMs. We introduce the In-Context Tense Attack(ITA) framework, a novel approach that employs harmful examples to undermine the integrity of LLMs. Our theoretical analysis elucidates how a constrained set of context examples can significantly influence the security mechanisms of LLMs. Through rigorous experimentation, we have substantiated the potency of ITA in elevating the success rate of jailbreaking prompts. On the PKU-Alignment/SafeRLHF dataset, ITA achieved a remarkable 92.99% increase in Accuracy, a 73.36% improvement in Rouge-L, and a 27.01% enhancement in Bleu-4 scores. Similarly, on the NVIDIA/Aegis-Safety dataset, ITA demonstrated a 72.03% increase in Accuracy, an 80.87% rise in Rouge-L, and a 40.24% boost in Bleu-4 scores. These results underscore the effectiveness of ITA in manipulating LLMs to generate harmful outputs, thereby highlighting the necessity for more robust security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 824-835页

作者： Min Yang Zhenwei Wang Qiancheng Xu Chengming Li Ruifeng Xu Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenChina School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA School of Intelligent Systems Engineering Sun Yat‐Sen UniversityGuangzhouChina School of Computer Science Harbin Institute of Technology(Shenzhen)ShenzhenChina

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.

关键词： deep learning emotional conversation generation semantic‐emotional memory

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Type-Representation Enhanced Event Detection via Structure Encoder 1

Type-Representation Enhanced Event Detection via Structure E...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Feng, Yuming Huang, Linmeng Liang, Haozhe Jin, Xiaolong Guan, Saiping Guo, Jiafeng School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Key Lab of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Beijing China

ISBN: (纸本)9798350358780

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset;Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task. © 2023 IEEE.

关键词： Signal encoding

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Interpretable Predictions for Crime Categories Using Log Loss Approach for Imbalanced Target Feature 2

Interpretable Predictions for Crime Categories Using Log Los...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Manasa, A. Sen, Snigdha Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383652

Crime indicates activities that infringe laws and regulations constituted by a governing authority. These wrongdoings can range from minor aberrations such as petty theft to more serious crimes like assault, robbery, or murder. Understanding crime patterns, causes, and trends is important for creating effective law enforcement strategies and enforcing policies that facilitate to a risk-free and stable society. This paper explores the interpretability of crime category predictions generated by XGBoost and Random Forest models on San Francisco crime data ranging 12 years. Notably, in comparison to other models, such as k-Nearest Neighbors (KNN), Stochastic Gradient Descent(SGD) and Naïve Bayes, our XGBoost and Random Forest models featured superior predictive excellence, Obtaining competitive log-loss scores of 2.277761 and 2.283956, respectively, to augment interpretability we have employed Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to clarify the decision-making processes of these models. This interpretability analysis would definitely add a deeper insight into the contributing factors of model's predictive behavior towards crime rate prediction. Additionally, we have dealt with imbalanced target feature as well. In comparison to existing research in the field, our work stands out by introducing advanced interpretability techniques, LIME and SHAP, enhancing transparency in crime prediction models. This innovative approach surpasses traditional methods, providing a more comprehensive understanding of crime dynamics and achieving superior predictive excellence. Innovation is introduced through the incorporation of advanced interpretability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). © 2024 IEEE.

关键词： Stochastic systems

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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