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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharathi Kannan, B. Sriramulu, Srinivasan Gopal, Vidhya Karthick, M. Reddy, Yanna Nithin Natarajan, B. Galgotias University School of Computing Science and Engineering India Sona College of Technology Computer Science and Engineering Salem India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350383287

The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provides dozens of IoT architectures. interoperability, security, privacy, dependability, energy limits, scalability, and a lack of shared standards are only some of the problems that have been recognized by the research community. The proposed IoT architecture should also consider issues of interoperability, scalability, dependability, and trust. By fixing these issues, this research work builds an IoT ecosystem that can accommodate a wide range of devices and services, guarantee smooth communication and scalability, and lay the groundwork for secure, dependable IoT offerings. In effective offloading and resulting interruptions in task execution might result from high latency or network outages. However, because of the importance of a select few delay-sensitive applications, it is important to guarantee their safety. Therefore, this research work aims to unload data at edge and cloud servers, Heuristic based Energy Efficient Data Rate Controller for Offloading has been developed for sensor networks. The bio-inspired NSGA-2 method is used to assess the performance of the suggested algorithm within the context of the problem's multi-objective function formulation. When compared to the current energy aware scheduling approach, the results show that the offloading method can decrease the amount of time and energy needed to run edge servers, maintain load balancing, and maximize resource utilization while still allowing applications that require delays to meet their deadlines. Finally, simulation results demonstrate the proposed approach outperforms 20% high efficiency compared to the

关键词： Internet of things

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Hybrid ABC-PSO Algorithm based Static Synchronous Compensator for Enhancing Power System Stability 10

Hybrid ABC-PSO Algorithm based Static Synchronous Compensato...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Ahmed Limon, Md. Faiyaj Hasan Shiblee, Md. Fazle Rouf, Abdur Iqbal, Md. Shahid Sylhet Engineering College Department of Electrical & Electronic Engineering Sylhet Bangladesh Sylhet Engineering College Department of Computer Science Engineering Sylhet Bangladesh

ISBN: (纸本)9798350318739

This paper introduces a Hybrid Artificial Bee Colony and Particle Swarm Optimization (HABC-PSO) approach for static synchronous compensator (STATCOM) implementation, aimed at improving system damping, addressing low-frequency and sub-synchronous oscillations, and enhancing overall power system stability. The proposed control strategy optimizes both the AC and DC voltage regulator's proportional-integral (PI) of the STATCOM using a time-domain objective function. To assess the effectiveness of this control scheme, we conducted steady-state and transient analyses using a kundur multi-area four-machine model. The simulation results were analyzed to assess the generator's rotor speed deviation, angle deviation, and bus-bar voltage deviation when using the HABC-PSO-based STATCOM. These results were then compared with scenarios both with and without the STATCOM, which included an examination of the settling times for rotor speed deviation (Δ ω) and angle deviation (Δ δ) of generators between the two areas. The simulation outcomes affirm that the proposed HABC-PSO based STATCOM is more resilient and efficient in mitigating inter-area oscillations, ultimately strengthening the power system stability. © 2023 IEEE.

关键词： Static synchronous compensators

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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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computer Systems Science & engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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ECO-Guard: An Integrated AI Sensor System for Monitoring Wildlife and Sustainable Forest Management 8th

ECO-Guard: An Integrated AI Sensor System for Monitoring Wil...

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8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Nikhilesh Krishna, Ch. Rauniyar, Avishek Kireeti Sai Bharadwaj, N. Raj, Sujay Bharath Valsan, Vipina Suresh, Kavya Ravikumar Pandi, V. Sathyan, Soumya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819994885

In India, 12% of wild animals are threatened with extinction, and human activity threatens more than 80% of East and Southeast Asia's wetlands. Every year, an estimated 400 people are killed by wildlife in India, with Maharashtra alone seeing 88 human deaths and 9258 cattle killings by tigers in 2020. The ECO-Guard is an intelligent device which uses the You Only Look Once (YOLO) model and various sensors and machine learning models with algorithms that support and promote forest monitoring and tourist guidance in forest areas addressing Sustainable Development Goal (SDG) 12 (Life on Land). The paper presents a linear chain-like mesh of devices speeded across bio-area which are used in correlation for intended surveillance. Each device can multimodule apps and tally their sightings automatically to analyze coherence of the situation with the forest rangers, organizations, and figures. It catches from the information, the ill substitutes like human spotting with beasts. It suggests that the advice can be further put into practice by constantly monitoring any closeness of alternatives that match to create an unfavorable combination. Also having to help detect many forest crimes, some including illegal logging, preventing, and deforestation on a large scale. This paper highlights a novel ECO-Guard, an intelligent and autonomous monitoring system which includes artificial intelligence (AI)-enhanced strategies and algorithms to conserve wildlife and forest ecosystems by integrating various hardware and software components. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deforestation

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Solving Secluded Path Problem by Heuristics and Local Search

Solving Secluded Path Problem by Heuristics and Local Search

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Acikalin, Utku Umur Caskurlu, Bugra Abul, Osman Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

Wireless Sensor Networks (WSNs) play a crucial role in various domains, from commercial to military, enabling precise monitoring of environmental and other conditions. However, the transmission of sensitive data in WSNs poses security challenges, particularly concerning eavesdropping attacks. While cryptographic methods offer protection, they often incur high energy costs, making them impractical for battery-powered WSNs. Our problem formulation, based on graph theory, reduces to the Secluded Paths problem for which no polynomial solution yet to known. Motivated by the challenge of reducing the eavesdropping risk through intelligent routing, we propose a scalable optimization approach using heuristics and local search techniques. We present a solution framework, followed by an experimental evaluation to demonstrate its efficacy. This paper contributes to the advancement of secure and efficient data transmission in WSNs. © 2024 ACM.

关键词： Sensitive data

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Blockchain Technology and Smart Contracts for Transformative Energy Trading 4

Blockchain Technology and Smart Contracts for Transformative...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Valsan, Vipina Sai, P. Sarvan Sri Mukesh, Koraganji Raju, Abbaraju Sree Tharun Sindukuri, Teja Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798331518981

The trend in renewable energy calls for safe peer-to-peer(P2P)energy trading in a microgrid. Blockchain technology (BCT) addresses this by providing a secure, tamper-proof ledger that enables transparent and sustainable energy exchange. This paper explores how smart contracts can make energy trading more efficient for microgrid users while securely recording all transactions for easy verification. The architecture designed for these smart contracts ensures reliable mechanisms such as simultaneous submission of bids, user registration, order placement and sorting, and automatic execution of the trading system. The proposed smart contract-based energy trading system improves energy efficiency and flexibility while supporting decentralized control within the microgrid. BCT directly contributes to the Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) by promoting this technology, increasing renewable energy in the global energy mix, and improving energy efficiency. Furthermore, BCT supports SDG 9 by creating a resilient and sustainable infrastructure through technological innovation. This paper provides a detailed analysis of the P2P trading scenario, smart contract pseudocode, and visualization of the smart contract implementation in Solidity, aiming to navigate the real-world application of P2P energy markets. © 2024 IEEE.

关键词： Smart contract

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A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions

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Multimedia Tools and Applications 2024年 1-58页

作者： Abdullahi, Mohammed Oyelade, Olaide Nathaniel Kana, Armand Florentin Donfack Bagiwa, Mustapha Aminu Abdullahi, Fatimah Binta Junaidu, Sahalu Balarabe Iliyasu, Ibrahim Ore-ofe, Ajayi Chiroma, Haruna Department of Computer Science Ahmadu Bello University Zaria Zaria Nigeria Department of Mechanical Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Engineering Ahmadu Bello University Zaria Nigeria College of Computer Science and Engineering University of Hafr Al Batin Hafar Al-Batin31991 Saudi Arabia School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models. © The Author(s), under exclusive licence to

关键词： computer vision

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Mobility-Aware Federated Learning-Based Proactive UAVs Placement in Emerging Cellular Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1305-1318页

作者： Manzoor, Sanaullah Shakir, Muhammad Zeeshan Hasna, Mazen O. Qaraqe, Khalid A. University of the West of Scotland School of Computing Engineering and Physical Sciences PaisleyG72 0LH United Kingdom Qatar University College of Engineering Doha Qatar Texas A and M University Electrical and Computer Engineering Program Doha Qatar

With the vast proliferation of smart mobile devices, there is an ever-increasing demand for higher data rates and seamless connectivity throughout. Current 5th generation and beyond (B5G) cellular networks struggle to eradicate outage zones and ensure seamless connectivity. One promising solution to this problem is the use of unmanned aerial vehicles (UAVs) to assist the traditional ground network and provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disasters such as flooding. In this paper, we propose a novel users' mobility-aware & users' demand-aware federated learning-based proactive UAV placement (MFPUP) framework to assist the existing ground communication network and minimise overall network outages. Our MFPUP framework utilises the federated learning-based mobility prediction model that recommends the potential outage areas to deploy UAVs using user-UAV association techniques such as the optimum association approach (OAP) and the greedy association approach (GAP). In order to validate the performance of the proposed MFPUP scheme we carried out extensive simulations. The proposed LSTM-based mobility model outperforms the DNN model with 92.88% prediction accuracy. Further, our results show that the proposed MFPUP framework associates the optimal number of users to UAVs while also improving 1.25 times users' downlink rates as compared other UAVs placement schemes. © 2023 CCBY.

关键词： Aircraft accidents

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Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions 31

Cultural Alignment in Large Language Models: An Explanatory ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Masoud, Reem I. Liu, Ziquan Ferianc, Martin Treleaven, Philip Rodrigues, Miguel Department of Electronic and Electrical Engineering University College London United Kingdom Department of Computer Science University College London United Kingdom Department of Electrical Engineering King Abdulaziz University Saudi Arabia AI Centre University College London United Kingdom

ISBN: (纸本)9798891761964

The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals and societies with diverse cultural backgrounds. While the discourse has focused mainly on political and social biases, our research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify cultural alignment using Hofstede's cultural dimension framework, which offers an explanatory cross-cultural comparison through the latent variable analysis. We apply our approach to quantitatively evaluate LLMs-namely Llama 2, GPT-3.5, and GPT-4-against the cultural dimensions of regions like the United States, China, and Arab countries, using different prompting styles and exploring the effects of language-specific fine-tuning on the models' behavioural tendencies and cultural values. Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions. Our study demonstrates that while all LLMs struggle to grasp cultural values, GPT-4 shows a unique capability to adapt to cultural nuances, particularly in Chinese settings. However, it faces challenges with American and Arab cultures. The research also highlights that fine-tuning LLama 2 models with different languages changes their responses to cultural questions, emphasizing the need for culturally diverse development in AI for worldwide acceptance and ethical use. For more details or to contribute to this research, visit our GitHub page https://***/reemim/Hofstedes_CAT. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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