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Informatica (Slovenia) 2025年第7期49卷 33-48页

作者： Benedict, Shajulin Subair, Rubiya Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India Technical University Munich Garching Germany

Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverless-based solutions, both of which are affordable. Treating the emotions of animals increases their productivity, especially among trained elephants when subjected to carrying logs or undertaking gargantuan tasks. However, existing infrastructures are inefficient in handling long-running animal emotion detection-related tasks. This article proposes a deep learning-driven edge-enabled serverless architecture after evaluating several existing animal emotion detection techniques. Additionally, we perform an exploratory study on the cost impact of incorporating serverless-enabled approaches to animal emotion detection architectures. We observed that the proposed edge-enabled serverless architectures saved over 13,000 dollars annually compared to traditional animal emotion detection approaches. In addition, the article provided a few research directions to develop novel edge-enabled serverless architectures that boost socio-economic situations while avoiding human-animal conflicts. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Invertebrates

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CTGAN-based oversampling and cost-sensitive deep neural network to predict smart money activity in stock market

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International Journal of Information technology (Singapore) 2025年第3期17卷 1489-1499页

作者： Baro, Pranita Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Assam Silchar 788010 India

Investment in the stock market has become a trend in today’s era. The primary force moving the market in a specific direction is the large buying and selling of hedge funds, pension funds, banks, etc. This paper proposes a deep learning strategy to predict smart money activity. Initially, a framework composed of technical analysis and quantitative analysis is considered to create a dataset. Then using this framework a comprehensive dataset is built from the data available in the National Stock Exchange of India (https://***/). In the proposed approach, minority samples in the dataset are first oversampled using Conditional Tabular Generative Adversarial Network based approach to even out class imbalance in the real-life dataset. Using the modified dataset, a cost-sensitive deep neural network is trained to predict smart money activity in the stock market. The proposed method is assessed using different evaluation metrics and the findings validate the superiority of the proposed methodology. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Conditional Tabular Generative Adversarial Networks Cost-sensitive Imbalance classification Neural network Smart money

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An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation

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International Journal of Web engineering and technology 2024年第2期19卷 110-126页

作者： Nagalakshmi, Bantupalli Subramanian, Sumathy School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employment. So, a multi-objective-based task scheduling for edge computing is suggested in this study. This paper develops the adaptive honey badger optimisation algorithm (AHBA) to accomplish this goal. The lack of population, the original honey badger algorithm (HBO) has the issue of becoming trapped in local optima. To maintain population variety and improve convergence towards the ideal solution, HBO is combined with the opposition-based learning technique (OBL). Based on makespan, cost, energy consumption, and resource usage, the multi-objective function is created. According to simulation results, the proposed approach has a lot of potential in this field. Java and cloud Simulator are used to implement the suggested model. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Energy utilization

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A no-code swarm simulation framework for agent-based modeling using nature-inspired algorithms

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International Journal of Information technology (Singapore) 2024年第7期16卷 4693-4699页

作者： Hasan, Ishraq Islam, Rubyeat Sharmin, Nusrat Md. Akhtaruzzaman Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Swarm robotics describes the coordination among multiple robots assigned to perform a single task collectively and work as a system. The system is usually used in search and-rescue missions in adverse natural environments. Research in this field involves testing theories and algorithms by using physical robots which requires expensive environment setup. This study presents a novel no-code simulation framework for swarm robotics. The software framework provides a set of building block of various components to customize the research and development process. Besides the various platforms that exist nowadays, this proposed framework overcomes the common problem of low flexibility in modeling, resulting significant improvements in the development of any simulation through customization of algorithms and multi-agent modeling. For rapid development of simulation instances, this study proposes a swarm simulation framework that allows multi-agent modeling in a no-code development environment by defining the number of agents and the behaviors they are expected to exhibit. A random environment is generated where the agents and specific target is defined to test swarm robots for search and rescue operations. The framework mainly proposes a no-code environment and provides the opportunity to test the swarm behavior and relevant theories with flexibilities to make necessary adjustments. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Multi-agent modeling No-code environment Simulation framework Swarm robotics

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Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Cloud 2nd

Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Clou...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lalitha Devi, K. Deepa Thilak, K. Shanmuganathan, C. Kalaiselvi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9789819995172

In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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Extensive examination of hyper-parameters setting using neural-based methods for limited resources language: Nyishi-English

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International Journal of Information technology (Singapore) 2024年第6期16卷 3901-3911页

作者： Kakum, Nabam Sambyo, Koj Department of Computer science and Engineering National Institute of Technology Arunachal Pradesh India

Taking advantage of the enormous advancement of the transformer model using a neural-based approach for text translation, we extent another research project into the extremely limited resources of the Indian language, Nyishi-English. In this work, we compare the quality of translation between the baseline RNN and Transformer model by employing the fine-tune hyperparameter optimization technique. Further, the quality of system prediction has been judged using both automatic metrics evaluation (BLEU) and human evaluation methods based on sentence length. Each parameter combination has been scrutinized, and the translation quality has been identified using BLEU. The human evaluation method has been used to verify the system prediction based on simple, complex and long sentences. The study concluded with the identification of the optimal hyperparameter optimization combination utilizing the limited resource language Nyishi. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BLEU Hyperparameters NMT Nyishi Transformer model

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On the simulation of hypervisor instructions for accurate timing simulation of virtualized systems

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International Journal of Information technology (Singapore) 2024年 1-10页

作者： Mhatre, Swapneel Chandran, Priya Department of Computer Science and Engineering National Institute of Technology Calicut Kerala India

Architectural simulators help in better understanding the behaviour of existing architectures and the design of new architectures. Virtualization has regained importance and this has put a pressing demand for the simulation of virtualized systems. Existing full-system simulators for virtualized systems simulate the application program instructions and the operating system instructions but abstract the hypercalls or traps to the hypervisor. This leads to inaccuracy in the simulation. This paper proposes an approach to simulate hypervisor instructions in addition to operating system instructions for accurate timing simulation of virtualized systems. The proposed approach is demonstrated by simulating RISC-V binary instructions. The simulator is an execution-driven, functional-first, software-based simulator coded in Python. Three programs—searching (linear search), sorting (bubble sort) and ***_ir workload from SPEC CPU2017 benchmarks are simulated using the developed simulator. It is seen that the number of instructions executed when the simulator is configured for a virtualized system are higher than the number of instructions executed when the simulator is configured for a non-virtualized system. This is due to the hypervisor instructions that get executed in a virtualized system. The paper concludes that the proposed approach leads to accurate timing simulation of virtualized systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Architecture Hypercall Hypervisor Operating system Simulation Virtualization

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Sentiment Analysis Unleashed: Leveraging VADER for Evaluation of Public Opinion 11

Sentiment Analysis Unleashed: Leveraging VADER for Evaluatio...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Arora, Suhawni Malhotra, Ashmit Kumar, Amit Singh, Ninni University Department of Computer Science Engineering & Technology Dehradun India Cmr Institute of Technology Department of Computer Science Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350350357

Digital reviews provide real-world feedback on products and services in an era of online commerce and access to information. Providing feedback fosters trust and credibility among potential customers, enabling them to make informed purchasing decisions. In review classification, sentiment analysis is a key component that assesses the emotional tone of the reviews. Businesses can prioritize responses, manage their reputation, and extract actionable insights by categorizing reviews as positive, negative, or neutral. The study also includes neutral feedback, which includes suggestions for incremental improvement, which assists in continuous product refinement. In the proposed work, more than 5,68,455 reviews are divided into positive negative, and neutral sentiments by sentiment analysis. The results demonstrate VADER's robustness and versatility, showing its capacity to accurately gauge public sentiment across different contexts and types of language, including slang, emojis, and colloquial expressions. We also compare VADER's performance with other sentiment analysis algorithms, underscoring its advantages in handling informal online communication. © 2024 IEEE.

关键词： Sentiment analysis

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Bio-Inspired Optimization Algorithm in Machine Learning and Practical Applications

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SN computer Science 2024年第8期5卷 1081页

作者： Juneja, Shallu Taneja, Harsh Patel, Ashish Jadhav, Yogesh Saroj, Anita Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Engineering SVM Institute of Technology Bharuch India ATLAS SkillTech University UGDX School of Technology Mumbai India

Software fault prediction is critical to ensuring system reliability and user satisfaction while minimizing financial losses. This research paper presents a novel and comprehensive technique for predicting software faults by integrating a Genetic Algorithm (GA) with an ensemble KNN and XG Boost algorithms classifier. The proposed approach is evaluated on 24 datasets, including 12 open-source Java projects and 12 NASA Metrics Data Program datasets, with the NASA Metrics Data Program datasets serving as macroscale datasets and open-source Java projects datasets serving as microscale datasets. By optimizing the selection and combination of fault-related metrics, the integrated GA on the ensemble classifier demonstrates enhanced accuracy in fault prediction compared to existing methods. The experimental results highlight the effectiveness of the proposed approach, establishing its superiority in predictive accuracy. This study significantly contributes to software engineering, offering an efficient technique for identifying and mitigating potential software faults, thereby elevating software reliability and quality assurance processes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic Algorithm JAVA open-source projects Machine learning NASA MDP PROMISE Stacking ensemble model of k-nearest neighbors (KNN) SVM XG boost

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An Optimal Disassembly Sequence Planning for Complex Products using Enhanced Deep Reinforcement Learning Framework

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SN computer Science 2024年第5期5卷 581页

作者： Chand, Mirothali Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Disassembly Sequence Planning (DSP) is a crucial problem in the field of repair and maintenance. There is a pressing need for an efficient technique to solve the Complete Disassembly Sequence Planning (CDSP) problem for large, highly complex products without compromising time and computational resources. Since exact methods fail to handle complex products, and meta-heuristic approaches often do not produce optimal results, its solution requires a deep reinforcement learning approach. This work proposes a novel Enhanced Deep Reinforcement Learning (EDRL) approach in which the Actor-Critic Networks with an attention mechanism is employed in both networks to assign weightage to important actions, generating optimal sequences based on these actions. To further improve the model and accurately predict the loss value, a hybrid loss function is developed by combining the Categorical Cross-entropy and Log-Cosh loss functions. This work considers DSP attributes like stability, liaison, geometric feasibility, and precedence for experiments on various products. The proposed EDRL-CDSP method outperforms existing techniques in terms of optimality while requiring less time to generate the solution. The optimized disassembly sequence, when followed, consumes less time for the total disassembly of all the parts of the product. These findings suggest that the proposed EDRL-CDSP approach can effectively address the challenges in DSP and provide practical benefits for industrial applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Disassembly Hybrid loss function Optimal sequence planning Optimization

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