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Energy Consumption in Wireless Sensor Networks Using Fruit Fly and Ant Colony Optimization Algorithms in Homogenous and Heterogeneous Environments 7

Energy Consumption in Wireless Sensor Networks Using Fruit F...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Neetu Gunjan Kaur, Amandeep Nit Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350350067

Wireless sensor networks (WSNs) is crucial in sensing environmental conditions in far-flung areas. However, their energy consumption remains a critical issue, affecting the network's lifetime and coverage area. Clustering has emerged as an efficient strategy to prolong sensor network lifespan, and the Fruit Fly Algorithm (FFA) and Ant Colony Optimization (ACO) are promising techniques for cluster formation and efficient path establishment, respectively. In this study, we propose an innovative approach that combines FFA for cluster formation and ACO for path establishment. This novel algorithm is implemented in MATLAB and evaluated in both homogeneous and heterogeneous environments. We compare our proposed algorithm with the Butterfly Optimization Algorithm (BOA) based clustering and Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Our findings suggest that the algorithm we proposed significantly outperforms both BOA based clustering and LEACH. It takes lifetime of Network and area of coverage, particularly in heterogeneous environments. © 2024 IEEE.

关键词： Ant colony optimization

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Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

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computer Systems science & engineering 2023年第5期45卷 2223-2234页

作者： Alla Abbas Khadir Seyed Amin Hosseini Seno Baydaa Fadhil Dhahir Rahmat Budiarto Department of Electrical Power Techniques Engineering Al-Hussain University CollegeKarbalaIraq Department of Computer Engineering Ferdowsi University of MashhadMashhadIran Department of Computer Science Thi-Qar UniversityThi-QarIraq Department of Computer Science Mercu Buana UniversityJakartaIndonesia

Fog computing became a traditional OffLad Destination(OLD)to compute the offloaded tasks of the Internet of Vehicles(IoV).Nevertheless,the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the ***,the IoV will incur additional offloading *** this paper,we propose a new offloading scheme by utilizing RoadSide Parked Vehicles(RSPV)as an alternative OLD for *** idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the ***,a performance evaluation of the proposed scheme has been presented and discussed with other benchmark offloading schemes.

关键词： RoadSide parked vehicles offloading cost deadline budget

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Optimizing Menstruation Symptom Management: A Machine Learning Approach to Personalized Nutrition Recommendations

Optimizing Menstruation Symptom Management: A Machine Learni...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Logapriya, E. Surendran, R. Sathish Kumar, J.P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals' day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly. © 2024 IEEE.

关键词： Support vector machines

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Comprehensive Analysis of Congestion Control Techniques for WSNs

Comprehensive Analysis of Congestion Control Techniques for ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Jakhar, Isha Vijarania, Meenu Singh, Hitesh Kumar, Vivek K R Mangalam University Computer Science Department Gurugram India Noida Institute Of Engineering & Technology Computer Science Department Noida India

ISBN: (纸本)9798350366846

Since Wireless Sensor Networks (WSNs) have limited resources for data processing, data storage, transmission capacity, and most crucially energy supply, congestion is one of the pervasive problems for WSNs. Over the past fifteen years, numerous survey operations have been conducted to look into and remedy a variety of congestion-related concerns and challenges. The majority of review articles that were published in the literature focused on traditional methods of congestion area management, such as resource-based, traffic-based, and hybrid methods. In this survey article, an effort has been made to provide a systematic evaluation of recent initiatives that have helped to improve the congestion control methodology in WSNs by taking into consideration traditional procedures, machine learning based approaches and nature inspired techniques. A comparative analysis of these techniques with other approaches has also been done to provide a clarity regarding the performance of the algorithms. In this paper, we have tried to highlight the key areas of the application of the algorithms along with their advantages and limitations. © 2024 IEEE.

关键词： Wireless sensor networks

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A Novel Delay Tolerant Routing Protocol for Opportunistic Networks 9

A Novel Delay Tolerant Routing Protocol for Opportunistic Ne...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Shah, Idris Afzal Ahmed, Mushtaq Yadav, Amit Mnit Jaipur Department of Computer Science And Engineering Rajasthan India

ISBN: (纸本)9798350394474

Delay Tolerant networks (DTNs) belong to a class of wireless networks which are designed specifically for environments where high latency, frequent disruptions and intermittent connectivity is seen. Such type of networks rely on a store-forward-carry mechanism in contrast to a store and forward mechanism as seen in MANETs and adhoc networks. In this paper we have attempted to propose a variant of Spray and Wait protocol based on the Catalan series. The Catalan spray and wait protocol converges faster than its binary counterpart and hence can find its applicability in applications with a higher packet delivery ratio at minimal cost. Simulation of real life scenario of cars and pedestrians moving on a map is used to show the working and efficacy of our proposed routing model. The Catalan algorithm outperforms the Binary Spray and Wait in term of metrics viz. delivery ratio, overhead ratio and average buffer time. It showcases a reduced overhead ratio of 21% when compared to the binary version of 24% for no of copies L = 18. The average buffer size and the number of copies used for 100, 500, 1000, and 2000 nodes are analyzed and the Catalan routing protocol exhibits a decrease of 0.1%, 1.5%,1.35%,1.38% (for last values) in average buffer occupancy as compared to the binary version. Moreover, it shows an overall average increase of 3% over Hexago and Pentago protocol in terms of delivery ratio. © 2024 IEEE.

关键词： Delay tolerant networks

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Dehazing using Generative Adversarial Network - A Review

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SN computer science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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A Cloud-Based, IoT-Enabled Health Monitoring System Based on Machine Learning Techniques

A Cloud-Based, IoT-Enabled Health Monitoring System Based on...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Walia, Mrigana Bathla, Gaurav Department of Computer Science and Engineering Chandigarh University Chandigarh India

ISBN: (纸本)9789819750801

In health care, the use of Cloud-Based and Internet of Things (IoT) technologies has resulted in more efficient and reliable health monitoring systems. The study presents research on a Cloud-Based, IoT-Enabled Health Monitoring System that uses Machine Learning (ML) methods to evaluate and predict health-related data. Several ML methods, such as K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB), are used to classify and interpret health data. A comparative analysis is done to establish their accuracy and precision rate performance. SVM and RF have the highest accuracy, which is 97.53% and 95.76%, while the precision rates are 95.95% and 95.90% in that order, making them the most effective algorithms for the health monitoring system, respectively. This study contributes to Cloud-Based, IoT-enabled healthcare systems by suggesting how ML can be used to create accurate health monitoring and prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Machine Learning Method for Optimal Route Calculation

Machine Learning Method for Optimal Route Calculation

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Singh, Nongmeikapam Thoiba Satwik, Vishwaroju Mani Wadhwa, Manoj Tirukala, Gowtham Pandey, Shradha Saxena, Smriti Srivastava, Hitanya Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350388534

This study unveils an innovative approach to optimizing routes by merging the capabilities of the BellmanFord algorithm with the adaptive nature of Q-Learning, addressing the dynamic obstacles encountered in route planning. By integrating the Bellman-Ford algorithm, renowned for its shortest path calculation even with negative weights, with the dynamic adaptability offered by Q-Learning, we introduce a method that surpasses the limitations of traditional routing strategies. Through a series of simulations, our proposed methodology showcases enhanced adaptability, efficiency, and precision in navigating dynamic environments, outperforming both conventional routing strategies and standalone machine learning approaches. The collaboration between Bellman-Ford and Q-Learning yields a robust solution for real-time routing challenges, making it indispensable for applications in logistics, autonomous vehicle navigation, and efficient traffic management. This contribution signifies a groundbreaking step in the domain of route optimization, offering a model that proficiently adjusts to evolving conditions, thus leading the way for future research and applications in this field. © 2024 IEEE.

关键词： Dynamic routing algorithms

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EmotionNet: Pioneering Deep Learning Fusion for Real-Time Speech Emotion Recognition with Convolutional Neural Networks 6

EmotionNet: Pioneering Deep Learning Fusion for Real-Time Sp...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Rahman, Md. Mominur Hossain, Md. Alamgir Hasan, Tahmid Ahmed, Md. Kawsar Sultana, Rabeya Islam, Md Shihabul Prime University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Speech emotion detection is driven by complex neurological mechanisms within the brain's nervous system, reflecting an individual's emotional state and psychological makeup. This unique characteristic serves as a foundation for many applications, including personalized user experiences, mental health diagnostics, and demographic analysis based on emotional responses. Traditional approaches to emotion recognition frequently face challenges such as variability in speech patterns, environmental noise, and speaker diversity. Within the evolving landscape of human-computer interaction, this research introduces a novel synthesis of Convolutional Neural Networks (CNNs) with the domain of speech emotion recognition, thus surpassing the boundaries of traditional methodologies. This research analyzed 957 audio samples from 5 individuals using benchmark datasets, extracting Mel Frequency Cepstral Coefficients (MFCC) to classify emotions accurately-including 'neutral', 'happy', 'angry', 'fear', 'surprise', 'calm', 'disgust', and 'sad'. Departing from traditional approaches reliant on manual feature engineering, our end-to-end model autonomously learns from raw speech, with our CNNs achieving an exceptional 96.7% accuracy. This marks a significant advancement in speech emotion recognition through deep learning, paving the way for more intuitive, emotion-aware human-computer interactions. © 2024 IEEE.

关键词： Emotion Recognition

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Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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