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Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

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International Journal of computers and Applications 2024年第9期46卷 715-727页

作者： Mohanty, Rasmita Kumari Sahoo, Satya Prakash Kabat, Manas Ranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for health monitoring, leveraging sensor nodes placed on or within the human body. This paradigm offers real-time insights into physiological parameters, enabling remote healthcare applications and enhancing patient well-being. However, the WBANs bring challenges that demand innovative solutions to ensure their seamless integration into healthcare ecosystems. Secondly, trustworthiness becomes paramount in healthcare applications, where the sensitivity of health-related data necessitates secure and reliable communication. Lastly, congestion issues, arising from bandwidth constraints and concurrent data transmissions, affect the performance of WBANs, impacting data integrity, energy consumption, and quality of service. Therefore, this paper proposes a TESLDAR protocol that incorporates the proposed Jellyfish Updated Aquila Narrow ExploExpand (JUANE) method aims to address issues such as hotspot-induced thermal stress, trustworthiness concerns, and congestion challenges. The proposed algorithm incorporates Jellyfish Optimization (JFO), inspired by the adaptable movement of jellyfish, with characteristics inspired by the Aquila bird. The method's innovative integration of jellyfish and bird-inspired behaviors positions it as a promising solution for efficient route maintenance in WBANs addressing thermal, trust, and congestion challenges for enhanced reliability in healthcare monitoring applications. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Energy efficiency

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Enhancing E-Commerce Text Classification: A GRU-Based Approach for Improved Product Understanding 5th

Enhancing E-Commerce Text Classification: A GRU-Based Approa...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Nazmul Abdal, Md. Shaown, Shahanaz Islam Islam, Khairum Aman, Rutba Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789819791118

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Revealing inputs causing web API performance latency using response-time-guided genetic algorithm fuzzing

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Artificial Life and Robotics 2024年第4期29卷 459-472页

作者： Huang, Ying-Tzu Lee, Shin-Jie Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Web APIs are integral to modern web development, enabling service integration and automation. Ensuring their performance and functionality is critical, yet performance testing is less explored due to the difficulty in detecting performance bugs. This paper presents a response time-guided genetic algorithm (GA) fuzzing approach to uncover web API performance latency in a black-box setting. Unlike traditional random input generation, our method uses GA to refine inputs through crossover and mutation, guided by response time-based fitness. We propose two seed generation methods: pairwise combinatorial testing using Mircosoft’s Pairwise Independent Combinatorial Testing (PICT) and randomly paired combinations. We compared our method with classic random fuzzing. Experiments on five real-world web APIs show that our approach significantly outperforms classic random fuzzing, identifying inputs with response times 1.5 to 26.3 times longer. Additionally, PICT-generated seeds demonstrated superior performance compared to randomly-paired combinations in 2 out of 5 APIs. Our findings highlight the potential of GA-based fuzzing to reveal web API performance latency, advocating for further research in this area. © International Society of Artificial Life and Robotics (ISAROB) 2024.

关键词： Genetic algorithms

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Artificial Intelligence-Driven Approach for Predicting Maternal Health Risk Factors 9

Artificial Intelligence-Driven Approach for Predicting Mater...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Hosaain, Mohammad Mobarak Kashem, Mohammod Abdul Nayan, Nasim Mahmud Department of Computer Science and Engineering Gazipur1707 Bangladesh Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350342482

The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability. Our models are highly predictive and have transparency and interpretability in their results because of our creative feature engineering, such as featurewiz and chi-square test, and meticulous data processing. The models underwent extensive validation procedures, and in three different categories, each proved better evaluation metrics results alongside accuracy levels above 99% in each class. Ensuring the durability and stability of the models, this remarkable degree of accuracy is further enhanced by ensemble techniques and hyperparameter tuning. Incorporating Explainable AI (XAI) methods, such as LIME and SHAP, has proven crucial in elucidating the models' decision-making process, fostering trust, and enabling their possible use in healthcare environments. The results highlight the potential of ML in predicting maternal health risks and establishing solid foundations for future integration with Medical Cyber-Physical Systems (MCPS), which has the potential to transform the field of predictive analytics in prenatal care completely. © 2024 IEEE.

关键词： Adversarial machine learning

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Minimum spanning tree cycle intersection problem on outerplanar graphs

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Discrete Applied Mathematics 2024年 343卷 328-339页

作者： Su, Tai-Yu Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan

We study the minimum spanning tree cycle intersection (MSTCI) problem on outerplanar graphs in this paper. Consider a connected simple graph G=(V,E) and any spanning tree T=(V,ET) of G, it is well-known that each non-tree edge e∈E∖ET induces a fundamental cycle in T∪{e}. We say that two distinct fundamental cycles intersect if they share at least one edge. The MSTCI problem seeks a spanning tree such that the number of pairwise cycle intersections is minimized. We provide a linear-time algorithm to solve the problem on outerplanar graphs by exploiting the cycle-cut duality on plane graphs. Besides, we show that the minimum cycle intersection number is bounded by O(n) for outerplanar graphs with n vertices. © 2023 Elsevier B.V.

关键词： Graphic methods

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Kernel-based Actor-Critic Learning Framework for Autonomous Brain Control on Trajectory

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 554-563页

作者： Song, Zhiwei Zhang, Xiang Chen, Shuhang Tan, Jieyuan Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

Reinforcement learning (RL)-based Brain-Machine Interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multi-step BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this paper, we propose a kernel-based actor-critic learning framework for multi-step BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial Prefrontal Cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multi-step brain control tasks. © 2016 IEEE.

关键词： Adversarial machine learning

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Approximate Constrained Discounted Dynamic Programming with Uniform Feasibility and Optimality

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IEEE Transactions on Automatic Control 2024年第6期70卷 4031-4036页

作者： Chang, Hyeong Soo Sogang University Department of Computer Science and Engineering Seoul121-742 Korea Republic of

An important question about finite constrained Markov decision process (CMDP) problem is if there exists a condition under which a uniformly-optimal and uniformly-feasible policy exists in the set of deterministic, history-independent, and stationary policies that achieves the optimal value at all initial states and if the CMDP problem with the condition can be solved by dynamic programming (DP). This is because the crux of the unconstrained MDP theory developed by Bellman lies in the answer to the same existence question of such an optimal policy to MDP. Even if the topic of CMDP has been studied over the years, there has not been any relevant responsive work since the open question was raised about three decades ago in the literature. We establish (as some answer to this question) that any finite CMDP problem Mc "contains"inherently a DP-structure in its "subordinate"CMDP problem Mc induced from the parameters of Mc and Mc is DP-solvable. We drive a policy-iteration-type algorithm for solving Mc providing an approximate solution to Mc or Mc with a fixed initial-state. © 1963-2012 IEEE.

关键词： Markov processes

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Investigating the Use of Automation in Cryptocurrency Trading 2

Investigating the Use of Automation in Cryptocurrency Tradin...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kaur, Inderpreet Chauhan, Yashica Gupta, Utsav Malik, Sagar Department of Computer Science and Engineering Uttar Pradesh India Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350371055

CryptoBot is a groundbreaking automated cryptocurrency trading system that answers the issues faced by traders in an environment where market dynamics change swiftly. Hence, CryptoBot employs a holistic approach of data gathering, preprocessing, predictive modelling, and real-time decision-making, thus it keeps analyzing real-time market data. The system then uses state of the art machine learning algorithms to make accurate predictions about price movements and determine when to enter and exit trades. This program also shows very promising results indicating a significant improvement in trading performance compared to traditional human-based strategies. CryptoBot therefore becomes an instrument of innovation for both experienced and non-experienced cryptocurrency investors. Thus, with its flexibility, precision, and smart automation users become more competitive reshaping how the investor interacts with the volatile but lucrative world of cryptocurrencies. As digital assets continue to reshape finance, CryptoBot stands at the forefront, exemplifying the future of intelligent trading. © 2024 IEEE.

关键词： Artificial Intelligence Automated Trading Cryptocurrency Machine Learning Market Analysis Price Prediction Risk Mitigation

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Unveiling Deceptive Intentions: Insights from Fixations and Pupil Size

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Proceedings of the ACM on Human-computer Interaction 2024年第ETRA期8卷 1-17页

作者： Foucher, Valentin Huckauf, Anke Department of General Psychology Faculty of Engineering Computer Science and Psychology Ulm University Germany

Recognition of deceptive intentions from the eyes has been of appealing interest in the last decades but is still unresolved. Here, we report the development of a paradigm based on the Concealed Information Test enabling the study of various kinds of deception, that is, faking and concealing. Based on a card game, we compared fixation as well as pupil behaviour while participants were instructed to fake, conceal, or tell the truth. We realized two different layouts of stimulus presentation. Fixations differed between concealing versus faking and telling the truth;pupil size additionally unveiled the object of deception. We infer that various kinds of deceptive behaviour must be carefully distinguished, and contribute to how to use gaze measures as indicators of deceptive intentions. © 2024 Copyright held by the owner/author(s).

关键词： Eye tracking

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