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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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Smart Road Damage Survey System Using Machine Vision and IoT technology 3

Smart Road Damage Survey System Using Machine Vision and IoT...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Mutijarsa, Kusprasapta Alunjati, Figo Agil Hidayat, Fadhil Kurnia, Ranna Arsyad, Zulkifli School of Electrical Engineering and Informatics Bandung Indonesia National Institute of Technology Department of Civil Engineering Bandung Indonesia Bandung State Polytechnic Department of Computer and Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung institute of technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively. © 2023 IEEE.

关键词： computer vision

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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Dynamic Day and Night Cycle Impact in a Serious VR Game 10

Dynamic Day and Night Cycle Impact in a Serious VR Game

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10th International Conference on Virtual Reality, ICVR 2024

作者： Damastuti, Fardani Annisa Firmansyah, Kenan Arif, Yunifa Miftachul Barakbah, Ali Ridho Hariadi, Mochamad Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Electronic Engineering Polytechnic Institute of Surabaya Department of Creative Multimedia Technology Surabaya Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Malang Indonesia Electronic Engineering Polytechnic Institute of Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350364231

The immersive nature of virtual reality (VR) gaming is significantly improved by the implementation of dynamic environmental systems. This paper focuses on the development and impact of a dynamic day and night cycle in a serious VR game. This feature is intended to simulate a full 24-hour cycle within a 10-minute gameplay session, resulting in a seamless transition between different times of day that impacts both the visual aesthetics and gameplay mechanics. The day and night cycle introduces strategic depth by requiring players to adjust their management strategies according to the time of day, which affects resource availability, operational efficiency, and conditions. This system not only enhances visual fidelity but also directly impacts gameplay, necessitating that player modify their strategies in response to the changing environment. Visual aesthetics are expected to increase by 15%, strategic profundity will be improved by 20%, and player immersion will be enhanced by 25%, according to the research findings. This investigation underscores the importance of natural cycles in enhancing the strategic complexity, interactivity, and realism of virtual reality environments, thereby offering valuable insights for the creation of future virtual reality games. © 2024 IEEE.

关键词： Virtual environments

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of Information technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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Performance Evaluation of IoT-Fog-Cloud System for Data Storage, Analysis and Visualisations Using Retrial Queues Approach 8th

Performance Evaluation of IoT-Fog-Cloud System for Data St...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Qurashi, Shahazad N. Goswami, Veeena Mund, G.B. Barik, Rabindra K. Department of Health Informatics College of Public Health and Tropical Medicine Jazan University Jazan Saudi Arabia School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9789819748945

The significance of the Internet of Things (IoT) is growing in the contemporary information age due to its quick acquisition of diverse data. Both the IoT and cloud computing systems generate vast quantities of data. Fog computing, a technique that employs leveraging, is utilised to process substantial volumes of data. Subsequently, the processed data are transmitted to a cloud storage system using fog nodes. The installation of an IoT-Fog-Cloud system leads to enhancements in both latency and throughput. The primary responsibility of the fog nodes inside the IoT-Fog-Cloud system is to perform localised collection, monitoring, and storage of IoT workload and data. This function serves to enhance efficiency and availability within the system. The discussion of compliance with Quality of Service (QoS) standards constitutes the pertinent subjects and challenges that necessitate attention in fog computing. The present article employs an M/M/1 retrial queueing model and analytical methodology to assess and examine the performance of the IoT-Fog-Cloud system, thereby offering a solution to the problem at hand. The mathematical methods employed in this study enable the determination of the service arrival required to meet the QoS criteria in IoT layer. It also provides mathematical expressions for various performance metrics of the system, including the arrival rate, service rate, retrial rate, and the average waiting time for requests in the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality of service

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation - Frequency Modulation multi-scale feature sets 24

An Explainable Artificial Intelligence model in the assessme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Nicolaou, Andria Kakas, Antonis Pattichis, Constantinos S. Pattichis, Marios S. Fotso, Kevin Loizou, Christos P. Pantzaris, Marios Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico United States Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

ISBN: (纸本)9798350339598

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models. © 2023 IEEE.

关键词： Frequency modulation

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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