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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lakshmi, M. Sri Sarma, Vanam Ajay G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798331509828

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Digital Twin Technology Electric Vehicle (EV) Battery Management Energy Efficiency Optimization Internet of Things (IoT) Predictive Maintenance

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An Optimal Feature Selection-Based Approach to Predict Cervical Cancer Using Machine Learning 3rd

An Optimal Feature Selection-Based Approach to Predict Cervi...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Al Mamun, Abdullah Uddin, Khandaker Mohammad Mohi Chakrabarti, Anamika Nur-A-Alam, Md. Mahbubur Rahman, Md. Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Cervical cancer has been one of the leading causes of female early death in recent years. In developing countries, cervical cancer occurs in more than 85% of cases. Cervical cancer has been associated with several risk factors. For forecasting the prognosis of cervical cancer patients, we created a prediction model in this study based on early screening and risk trends in individual health records. In this paper, we analyze the risk variables for cervical cancer using ML classification methods. The study’s dataset is very unbalanced and includes missing values. Consequently, the ROS approach, a sampling method was used. The effectiveness of class imbalance was demonstrated by comparing the suggested model’s accuracy, sensitivity, and specificity. The RF, GB, and MLP machine learning models perform better with 99.07, 98.57, and 98.51% accuracy when using the Random Oversampling technique, and XGBoost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Embeddings Between State and Action Based Probabilistic Logics

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Formal Aspects of Computing 2025年第2期37卷 1-58页

作者： Das, Susmoy Sharma, Arpit Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Madhya Pradesh Bhopal India

This article defines embeddings between state-based and action-based probabilistic logics which can be used to support probabilistic model checking. First, we slightly modify the model embeddings proposed in the literature to allow invisible computation steps and the preservation of forward and backward bisimulation relations. Next, we propose the syntax and semantics of an action-based Probabilistic Computation Tree Logic (APCTL) and an action-based PCTL∗(APCTL∗) interpreted over action-labeled discrete-time Markov chains (ADTMCs). We show that both these logics are strictly more expressive than the probabilistic variant of Hennessy-Milner logic (prHML). We define an embedding aldl which can be used to construct APCTL∗formulae from PCTL∗formulae and an embedding sldl from APCTL∗formulae to PCTL∗formulae. Similarly, we define the embeddings and from PCTL to APCTL and APCTL to PCTL, respectively. We also define the reward-based variant of APCTL (APRCTL) interpreted over action-based Markov Reward Models (AMRM), and accordingly modify the logical embeddings and which allows us to take into account the notion of rewards. Additionally, we also show that the idea of rewards can be used to reason about the bounded until operator in PCTL and APCTL. Finally, we prove that our logical embeddings combined with the model embeddings enable one to minimize, analyze, and verify probabilistic models in one domain using state-of-the-art tools and techniques developed for the other domain. In order to validate the efficacy of our theoretical framework, we apply it to two case studies using the probabilistic symbolic model checker (PRISM). © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Probabilistic Markov chain equivalence bisimulation logic embeddings rewards model checking

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Enhancing EmoBot: An In-Depth Analysis of User Expectation and Satisfaction in an Emotion-Aware Chatbot 24

Enhancing EmoBot: An In-Depth Analysis of User Expectation a...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Mubassira, Taseen Hasan, Mehedi Noor, Jannatun Alim Al Islam, A.B.M. Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (纸本)9798400711589

The research community has traditionally concentrated on emotion detection in emotion modeling, while emotion generation has garnered less focus. With the rise of artificial intelligence, numerous chatbots have been developed, but there remains a lack of standardized methods to evaluate them. In this study, we evaluate EmoBot, a state-of-the-art emotionally aware chatbot, through the lens of user expectations. By collecting and analyzing user feedback, we identify EmoBot’s strengths and weaknesses, providing a basis for future enhancements. We propose targeted improvements to enhance its user experience, especially in areas of underperformance, and offer a framework for assessing emotionally aware chatbots based on user expectations and experiences. © 2024 Copyright held by the owner/author(s).

关键词： Emotionally Aware ChatBot

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LBPNET: Detection of Fake Face Currency through LBP-Based Convolutional Neural Networks 5

LBPNET: Detection of Fake Face Currency through LBP-Based Co...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Reddy, Y.V. Bhaskar Kumar, C. Satya Ashok, Rayalla Mandlem, Sai Nithin Reddy, P. Ashok Reddy, M.Babu Department of Computer Science and Engineering Mylavaram India Vardhaman College of Engineering Department of Computer Science and Engineering India Krishna University Department of Computer Science Machilipatnam India

ISBN: (纸本)9798331522100

The escalating sophistication of counterfeit face currency demands innovative solutions for robust detection. In this study, we propose LBPNET, a novel approach employing Local Binary Pattern (LBP) feature extraction and Convolutional Neural Networks (CNNs) to address the challenges posed by realistic fake currencies. LBP descriptors are extracted from currency images, capturing subtle patterns invisible to the human eye. These descriptors serve as inputs for a CNN, named LBPNET, trained to discern genuine from counterfeit face currencies. The model undergoes a comprehensive training phase, incorporating diverse currency images, to establish a generalized and adaptive detection system. In testing, new currency images are evaluated using the trained LBPNET to identify the presence of fake or non-fake currency, showcasing the model's adaptability to emerging counterfeit variations. This approach integrates machine learning and image processing to enhance detection capabilities, offering a promising solution for the evolving landscape of counterfeit face currency across forensic and social media domains. © 2025 IEEE.

关键词： Feature Selection

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Agricultural supply chain management using hyperledger and AIOT

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Journal of Ambient Intelligence and Humanized Computing 2025年第4期16卷 471-485页

作者： Jha, Anurag Kumar Raj, Aparna Jha, Ashish Kumar Shetty, Sujala D. Department of Computer Science Bits-Pilani Dubai Campus Dubai United Arab Emirates Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

Supply chain management and Hyperledger are two interconnected domains. They leverage blockchain technology to enhance efficiency, transparency, and security in supply chain operations. Together, they provide a decentralized, traceable, and real-time platform for recording and managing transactions. This combination is particularly valuable for industries dealing with sensitive goods, as it provides accurate traceability and real-time information. This paper explores the integration of supply chain management with Hyperledger blockchain technology to enhance efficiency, transparency, and security in supply chain operations. We propose a decentralized Hyperledger Fabric blockchain network to improve traceability, security, and efficiency by monitoring environmental conditions. This approach is particularly beneficial for transporting sensitive goods, such as medical supplies and perishable items, by ensuring optimal conditions and real-time data accessibility. The integration of Artificial Intelligence (AI) further enhances insights, reduces waste, and improves overall efficiency. By utilizing a distributed network free from third-party intermediaries, the system ensures immutability and remote accessibility, addressing challenges related to transporting heat and humidity sensitive products. Our experimental assessment demonstrates the benefits of private blockchain technologies, including enhanced security, regulatory compliance, compatibility, flexibility, and scalability. This study presents a detailed methodology for developing a traceable, efficient, and sustainable agricultural supply chain. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Efficiency

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Explainable cost-sensitive deep neural networks for brain tumor detection from brain MRI images considering data imbalance

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Multimedia Tools and Applications 2025年 1-28页

作者： Shawon, Md Tanvir Rouf Shibli, G. M. Shahariar Ahmed, Farzad Joy, Sajib Kumar Saha Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time required for manual review of the report and create an automated system for classifying brain tumors. An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable AI approaches are incorporated to visualize the model’s latent behavior in order to understand its black box behavior. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets which has achieved almost 4% more accuracy than the conventional models used in our experiments. The cost-sensitive InceptionV3 (CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a recall value of 1.00 respectively on an imbalanced dataset. The proposed models have shown great potential in improving tumor detection accuracy and must be further developed for application in practical solutions. We have provided the datasets and made our implementations publicly available at -https://***/shahariar-shibli/Explainable-Cost-Sensitive-DeepNeural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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