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25th International Arab Conference on Information technology, ACIT 2024

作者： Ababneh, Jafar Abu-Jassar, Amer Abuowaida, Suhaila Liubchenko, Valentyn Lyashenko, Vyacheslav Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Amman Arab University College of of Computer Sciences and Information Amman Jordan Al Al-Bayt University Faculty of Information Technology Department of Computer Science Al-Mafraq Jordan Kharkiv National University of Radio Electronics Department of Informatics Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798331540012

The research presents a comparative analysis of the selection of objects of interest in color medical cytology images. For these purposes, the individual edge detection operators studied are Canny, Roberts, and LoG. The comparison contrasts the standard method of assessing color photographs with a technique that incorporates supplementary information. Data from individual color channels, derived from the breakdown of a color image in RGB format, serve as supplementary information. The following individual cases for potential examination are emphasized: Image processing with a complete level of reuse of edge detection operators;with a partial level of reuse of edge detection operators. The results are displayed through an array of tables, graphs, and images. Metrics of visual perception of images are utilized for comparative analysis. Compared with traditional edge detection methods, the proposed approach yields better results, which can exceed the estimates of the classical approach by 20%. © 2024 IEEE.

关键词： Color photography

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Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

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computer Systems Science & engineering 2022年第12期43卷 1057-1068页

作者： S.Priyadarsini Carlos Andrés Tavera Romero Abolfazl Mehbodniya P.Vidya Sagar Sudhakar Sengan Department of Computer Science and Engineering P.S.R.Engineering CollegeSivakasiTamil Nadu626123India COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyKuwait Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra Pradesh522502India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamil Nadu627152India

In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.

关键词： Computed tomography contrast enhancement adaptive edge modeling multi-angle texture active contour liver tumor segmentation

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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

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computers, Materials & Continua 2023年第5期75卷 2347-2363页

作者： Sohaib Manzoor Hira Manzoor Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Electrical Engineering Mirpur University of Science and Technology(MUST)Mirpur-10250(AJK)Pakistan Faculty of Electronics Telecommunications and InformaticsGdansk University of Technology80-233 GdanskPoland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

关键词： Association multi-criterion SDN Wi-Fi

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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An Enhanced Packet Delivery Ratio Improvement Protocol Design over 5G Assisted Wireless Sensor Network Environment 2

An Enhanced Packet Delivery Ratio Improvement Protocol Desig...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Balaji, T.S. Charaan, R.M.Dilip Rajalakshmi, J. Samuthira Pandi, V. Shobana, D. Priya, J. Lakshmi Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India S.A.Engineering College Department of Cse Tamilnadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Sensors are pervasive in modern life, and Wireless Sensor Networks (WSNs) will play an increasingly important role in the years to come. Broadband, energy, coverage, and route optimization resource shortages are WSN's main drawbacks. Optimizing the packet delivery ratio is a crucial aspect of wireless sensor network (WSN) architecture. In order to enhance WSNs over the long term, previous approaches used a secure zone-based routing mechanism. For packet transmission, a new criterion was developed in multi-hop communication. It was built into the routing metric to protect against flooding, message manipulation, and dropping attacks. Congestion in WSN increases and reduces packet delivery ratio, even while energy conservation and attack resilience are accomplished. Our proposed solution, Advanced Path Selection with Traffic Estimation (APSTE), is a new way of routing data packets that takes flow differentiation into account. This should help increase the packet delivery ratio and solve this issue. In order to determine how effective, the suggested method is, it is cross-validated using the standard routing model known as Link Consistency with Path Evaluation (LCPE). In order to improve the WSN's QoS parameters and extend the network's lifespan, the APSTE approach reduces the energy consumption of sensor nodes while maintaining or improving their logical throughput and packet acquisition rates. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhanced Circular Parasitic Element at Directional Wideband Wearable Antenna for Microwave Breast Imaging Applications 10

Enhanced Circular Parasitic Element at Directional Wideband ...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Koma'rudin, Nur Atika Binti Salim, Norhuda Binti Zakaria, Zahriladha Hassan, Nornikman Soh, Ping Jack Department of Human Resource Bandar Vendor Taboh Naning Alor Gajah Melaka78000 Malaysia Faculty of Electronics and Computer Technology and Engineering Hang Tuah Jaya Durian Tunggal76100 Malaysia Oulu90014 Finland

ISBN: (纸本)9798350364163

A 3.06 GHz and 7.0 GHz wideband wearable antenna for microwave imaging (MWI) in breast cancer applications has been developed, involving design, simulation, fabrication, and measurement. Simulations for Antenna A, Antenna B, and Antenna C were conducted using CST Microwave Studio. The wearable antenna is intended for MWI breast cancer diagnosis. A hemisphere breast phantom with three fat layers, a round tumor, and skin (thicknesses of 68 mm, 5 mm, and 2 mm) was created and measured. Data from the breast phantom was analyzed using a modified delay and sum (mDAS) imaging technique in MATLAB to identify the tumor's location. For simulation of Antenna C, it shows the resonances at 3.07 GHz and 5.15 GHz with - 37.15 dB and - 38.51 dB, respectively. For measurement, it stated that a 3.29 GHz, 5.51 GHz, and 6.67 GHz, with return losses of - 24.67 dB, - 17.65 dB, and - 19.62 dB, respectively. © 2024 IEEE.

关键词： Phantoms

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SVM Classifier for Hygienic Food Handling with Cloud-Connected UV-C Sterilization Solutions 10

SVM Classifier for Hygienic Food Handling with Cloud-Connect...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Arunfred, N. Ranganathan, Chitra Sabapathy Kumaresh, P.S. Maruthukannan, B. Gobinath, D. Arunagiri, Bhuvaneswari Srm Institute of Science and Technology Faculty of Management Studies Chengalpattu Chennai Tamil Nadu Kattankulathur India Mphasis Corporation ChandlerAZ United States K.L.N.College of Engineering Department of Electronics and Communication Engineering Sivagangai Tamil Nadu Pottapalayam India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research integrates a support vector machine (SVM) classifier with cloud-connected ultraviolet-C (UV-C) sterilization systems to improve food handling hygiene. To ensure food safety, this system uses machine learning to analyze ambient conditions, handling processes, and equipment cleanliness. The SVM classifier classifies hygiene levels based on input data for real-time monitoring and decision-making. Cloud-connected UV-C sterilization technologies automate and streamline food industry microorganism control. Remote monitoring and control via the cloud lets stakeholder's access vital data and take immediate action. This increases food handling cleanliness, system speed, and responsiveness. The research examines the reduction of microbial contamination in various settings by using the SVM classifier and the cloud-connected UV-C sterilization solutions. Results show a considerable increase in hygiene level predictions and a significant decrease in the presence of bacteria in food processing. The SVM classifier and cloud-connected UV-C sterilization systems might improve food safety standards. Machine learning and cloud technologies provide a complete and adaptable solution to sanitary food handling issues, providing customers with safe and high-quality food items. © 2024 IEEE.

关键词： Remote control

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Digital Twin Driven Intelligent Systems and Emerging Metaverse 1

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2023年

作者： Enis Karaarslan Ömer Aydin Ümit Cali Moharram Challenger

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Numerical solution of the problem of turbulent currents that occur in the case of edge hardening 15

Numerical solution of the problem of turbulent currents that...

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15th International Conference on electronics, computers and Artificial Intelligence, ECAI 2023

作者： Codrean, Marius Novac, Mihaela Cornelia Codrean, Mihaela Gordan, Cornelia Emilia Novac, Ovidiu Constantin Bujdoso, Gyongyi Slovac, Francisc University of Oradea Department of Electrical Engineering Electrical Engineering and Information Technology Faculty Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania University of Oradea Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary

ISBN: (纸本)9798350321388

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade. © 2023 IEEE.

关键词： Induction heating

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A Supervised Machine Learning Approach for Short-Term Forecasting and Demand Response Optimization for Enhancing Energy Flexibility in Smart home 3

A Supervised Machine Learning Approach for Short-Term Foreca...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Glaret Subin, P. Gomathy, V. Ramasamy, Krishnakumar Dhivya, R. Arun Kumar, U. Shargunam, S. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Vadapalani Campus Tamilnadu Chennai600026 India Kathir College of Engineering Department of Eee Tamilnadu Coimbatore641062 India Sri Ramakrishna Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641022 India M.Kumarasamy College of Engineering Information Technology Tamilnadu Karur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Tamilnadu Srivilliputhur India

ISBN: (纸本)9798350375190

This research investigates supervised machine learning algorithms for short-term energy forecast in smart homes. A dataset involving energy consumption in the house was used and it was analyzed across 10-times using the Support Vector Machine, k-Nearest Neighbors, Gradient Boosting Machines, and Ensemble Methods. Results show that the performance of the Support Vector Machine was significantly higher with a mean, standard deviation, and maximum accuracy of 0.84, 0.02, and 0.88. Notably, the SVM had the minimum mean absolute error of 0.035 and root mean square error at 0.045. The coefficient of determination was also high, between 0.72 and 0.76, across 10-times of iteration. Thus, the SVM model is more accurate and reliable for forecasting energy consumption in the house. Although k-NN, GBM, and Ensemble Methods also showed considerable success, the abovementioned information proves that SVM is the most beneficial algorithm for smart home energy forecasting for the short-term. These outcomes can help design efficient energy management systems as they inform practitioners about the effectiveness of this method for energy consumption optimization to decrease costs and increase user comfort. Knowing which algorithms are capable of producing the high-quality results helps to design highly efficient short-term and long-term energy management solutions that satisfy the needs and preferences of smart home users, making the energy as sustainable and efficient as possible. © 2024 IEEE.

关键词： Energy utilization

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