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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Tegegn, Eyob Abayneh Beza, Teketay Mulu Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

Micro-grid power systems that utilize renewable energy sources, such as solar PV and wind turbines, are a viable solution for providing electricity to remote areas that are not connected to national grids. By combining various renewable energy sources and utilizing battery storage and backup diesel generators, these hybrid systems can provide reliable off-grid power. This paper focused on optimal sizing and feasibility study of a micro-grid system consisting of solar PV, wind turbines, battery banks, diesel generator, and a converter for the rural area of Gashamo village, located in the eastern part of Jarar Zone in the Somali Region of Ethiopia. The hybrid system was designed based on the primary load demand of the village, which was 2895 kWh/day, with a peak load of 176 kW, deferrable energy of 225 kWh/day, and a deferrable peak load of 37 kW, with a COE of $0.162/kWh. The HOMER (Hybrid Optimization Model for Electric Renewable) software tool was utilized to simulate and optimize the off-grid system and energy management system. Solar PV and wind energy were considered as primary sources to supply electricity directly to the load and to charge the battery bank when excess energy generation was available, with the diesel generator serving as a backup. During the design of the power system setup, simulation and optimization were conducted based on load demand, climatic data, economics of integrated system components, and other parameters, with the aim of minimizing the total NPC and levelized COE to select an economically feasible and technically capable hybrid power system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wind power

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A Hybrid Embedding Approach to Classify Crisis-Related Short Text 12th

A Hybrid Embedding Approach to Classify Crisis-Related Short...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Nath, Basudev Sahoo, Deepak Paul, Nayan Ranjan Faculty of Engineering Technologies Sri Sri University Cuttack India Department of Computer Science and Engineering Silicon Institute Technology Bhubaneswar India

ISBN: (纸本)9789819788354

High mobile phone usage and internet access on phones make it simple to connect to the globe and share your thoughts, feelings, and views on local or global issues on social media. This social media content helps government and corporate institutions grasp public opinion. People utilise social media to report disasters and request medical, food, and water aid. It is difficult to recognise disaster-related tweets and determine people's needs. This paper proposed a hybrid model to recognise disaster-related tweets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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Artificial Intelligence-Based Secured Smart Mirror Control Systems with Enhanced Vehicle and Signboard Detection 11th

Artificial Intelligence-Based Secured Smart Mirror Control S...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Sanjay, S. Nithish Soorya, S.S. Vengatesh, R. Prakasam, P. Annamalai, Pushparaghavan School of Electronics Engineering Vellore Institute of Technology Vellore India Faculty of Electrical and Computer Engineering Bahirdar University Bahir Dar Ethiopia

ISBN: (纸本)9783031641503

Vehicle accidents have a significant impact on society, and they have a number of detrimental repercussions on individuals, families, and communities. Vehicles are an essential part of everyone’s daily lives in the modern world;therefore, it is very important to drive them in a cautious manner to prevent accidents. A side mirror is very important equipment in a vehicle as it helps drivers with a view of the area behind and to the sides of the vehicle. The proposed work focuses on the enhancement of artificial intelligence (AI)-based automatic side mirror folding systems in addition with vehicle and signboard detection as driver alert measures. The system proposed inspects authorized persons with the help of face detection and fingerprint verification applications provided via individual phones. The signboard detection helps the driver to notice signboards to make necessary adjustments in very crowded traffic areas, and vehicle detection helps the driver to drive in a cautious manner, specifically when taking turns in highly congested areas with the help of Internet of Things (IOT) applications. The system checks for the presence of signboards, monitors the surroundings, and supervises the security of the vehicle using face detection and android app-based fingerprint verification. The proposed AI-based method achieved the 95.5% testing accuracy for unregistered persons with the help of face detection and fingerprint verification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mirrors

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Robot Arm Control System via Ethernet with Kinect V2 Camera for use in Hazardous Areas 1

Robot Arm Control System via Ethernet with Kinect V2 Camera ...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Suphalak, Kanok Klanpet, Nattapol Sikaressakul, Nuttakit Prongnuch, Sethakarn Suan Sunandha Rajabhat University Faculty of Industrial Technology Department of Computer Engineering Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Industrial Technology Department of Robotics Engineering Bangkok Thailand

ISBN: (纸本)9798350348842

This paper introduces the Ethernet robot arm control system with Kinect V2 camera for use in hazardous areas. The goal is to evaluate the performance of Ethernet communication and robot arm control in picking and placing objects. This system is divided into two architectures. Hardware includes an AMBER B1 robot arm and gripper, a Kinect V2 camera, AMBER robot arm control system, a router, and a PC/laptop. Software includes the C# software client, ROS2, Kinect for Windows SDK 2.0, and ARS. The experiment is separated into two parts: An Ethernet UDP performance evaluation and an object pick and place test. Maximum Ethernet UDP bandwidth is 4.20 Mbits/sec and there is no packet loss. There is a 15% error for picking objects. The movement of the robot arm follows the user's movement and object placement with 100% success. The robot arm control system from this research can help access dangerous areas after accidents. © 2024 IEEE.

关键词： Ethernet

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Forecasting and managing urban futures: machine learning models and optimization of urban expansion

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Asian Journal of Civil engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

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The Impact of Enhancing the k-Means Algorithm through Genetic Algorithm Optimization on High Dimensional Data Clustering Outcomes

The Impact of Enhancing the k-Means Algorithm through Geneti...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Symbiosis Institute of Business Management Nagpur India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of unsupervised machine learning, the k-Means algorithm stands as a cornerstone for clustering high-dimensional data. However, its efficiency and accuracy can significantly dwindle as the dimensionality of the dataset increases. This paper introduces an innovative approach that integrates Genetic Algorithm (GA) optimization with the k-Means clustering process, aiming to enhance its performance on high-dimensional datasets. The proposed methodology leverages the evolutionary capabilities of Genetic Algorithms to optimize the initial centroid selection and clustering configuration of k-Means, thus addressing its sensitivity to initial conditions and potential to converge to local optima. We systematically detail the implementation of this hybrid algorithm, incorporating mathematical expressions to elucidate the optimization process. Through extensive experiments on synthetic and real-world datasets, we demonstrate the superior clustering outcomes of the enhanced k-Means algorithm in terms of accuracy, robustness, and computational efficiency. The results are presented via a series of graphs and tables, providing a comparative analysis against traditional clustering algorithms. Our findings indicate that the integration of GA into the k-Means algorithm significantly improves its performance on high-dimensional data, making it a powerful tool for data mining applications where conventional clustering methods fall short. This study not only proposes a novel clustering solution but also contributes to the ongoing discourse on optimizing machine learning algorithms for complex datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Secured IIoT against trust deficit - A flexi cryptic approach

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Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

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IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Image-Based Automatic Energy Meter Reading Using Deep Learning

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computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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