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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vivek, V. Kumar, Aditya Yuvaraj, S. Vaishali, A. Katyal, Ashu Sahoo, Girija Shankar Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper affords a progressive approach to high-pace wide area fiber optic (WAFO) communications by way of combining radio frequency (RF) and optical spectral optimization strategies. This method presents a new approach to the longstanding task of reaching high-pace, long-distance transmission over current networks. The optimization consists of maximizing the spectral performance of the RF and optical structures with the aid of the use of techniques which includes frequency-agile modulation codec's, linear dispersion compensation, and spectral shaping strategies. Moreover, dynamic optical and RF network routing algorithms are proposed to enhance potential and reduce latency. The proposed optimization is confirmed thru enormous simulation and numerical analysis. Results indicate big overall performance gains in phrases of spectral performance, records price, postpone, and reliability. This joint RF-optical optimization approach is an efficient and value-effective answer for WAFO networks and ought to notably benefit large-scale and future WAFO deployments. © 2023 IEEE.

关键词： Compensation Deployments Enormous Performance Reduce

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Impact on Higher Education and College Students in Dijlah University after COVID through E-learning

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computer-Aided Design and Applications 2023年第S12期20卷 104-115页

作者： Khudhair, Abdulmunem A. Khudhair, Muthanna A. Jaber, Mustafa Musa Awreed, Youssef Jalil Ali, Mohammed Hasan Al-Hameed, Mazin R. Jassim, Mustafa Mohammed Malik, R.Q. Alkhayyat, Ahmed Hameed, Asaad Shakir Department of Computer Science Dijlah University College Baghdad10021 Iraq Department of information technology Almashreq University Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf10023 Iraq Computer Technical Engineering National University of science and technology Thi Qar Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad10011 Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq College of Technical Engineering The Islamic University Najaf Iraq Performance Quality Department Mazaya University College Thi-Qar Iraq

The higher education industry in Iraq had various challenges during the covid-19 outbreak, and this study looked at how remote learning and other relevant technology helped. During the new standard period, a platform with other internet providers helped to keep this sector afloat. The Corona crisis (COVID-19) had a huge impact on Iraqi education, which was revolutionized through the use of e-learning to lessen the danger of pupils contracting COVID-19. learning and its related technology platform, and other online outcomes enabled Iraq's critical higher education businessto survive the covid-19 crisis while also providing insight into the sector's difficulties in the new normal age. © 2023 CAD Solutions, LLC.

关键词： E-learning

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Agarwal, Ankita Aravindan Shrivastava, Manish Sathya, S. Verma, Devika A. Sohal, Jagmeet Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Centre of Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN). © 2023 IEEE.

关键词： Recurrent neural networks

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A Detailed Review on Control Strategies Used in Grid System Which Isused for Independed System

A Detailed Review on Control Strategies Used in Grid System ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Savitha, R. Singh, Baldev Singh, Anjali Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Clean and environmentally friendly power sources are in high demand given the focus on conscious living, concerns about the environment, and the depletion of fossil fuel reserves in modern times. The notion of a microgrid becomes a crucial pathway towards the exploration of sustainable and eco-friendly energy sources. The deployment of an Ad/DC microgrid is a practical option that satisfies the needs of both DC and AC microgrids, considering the effectiveness of the Rc energy system and the growing need for powering essential loads. The operational challenges that arise in AC/DC microgrids due to factors such as intermittent energy production from renewable sources, main grid failures, and uncertainties in variation in load highlight the importance of synchronizing and connecting diverse power converters effectively. The recognition of these obstacles functions as a guide for further research initiatives with the goal of creating dependable and sustainable control systems for AC/DC microgrids. Overall, this article is a great resource that provides a broad overview of the developing area and opens the door for further advancements in the field of energy-efficient and environmentally friendly systems. © 2023 IEEE.

关键词： AC/DC Microgrids Constant Current Control Constant Voltage Control Control Strategies DC Droop Control and PQ Control Grid-Connected Interlinking Power Converter Power Converters Renewable Energy Sources

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Forecasting New Mobile Site Location Suitability Using Machine Learning 8

Forecasting New Mobile Site Location Suitability Using Machi...

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Rabbani, Zeeshan Ebrahim Hosseini, Seyed Pervez Chattha, Shahbaz Alandjani, Gasim Abosaq, Nasser Abdul Majid, Mazlina School of Information Technology Whitecliffe College Auckland New Zealand School of Information Technology Whitecliffe College Christchurch New Zealand School of Information Technology Whitecliffe College Wellington New Zealand Yanbu Industrial College Computer Science and Engineering Yanbu Saudi Arabia Universi Malaysia Pahang Al-Sultan Faculty of Computing Abdullah Pekan Malaysia

ISBN: (纸本)9798331539696

The increasing density of mobile networks has made the identification of optimal new coverage sites a critical challenge for mobile operators. Traditional methods relying on manual inspections and council plans are labour-intensive and insufficient. This study addresses this issue by leveraging data science and machine learning techniques to predict the suitability of new mobile cell sites. Utilizing data from Radio Spectrum Management New Zealand and Stats New Zealand, the study employs Random Forest as Machine learning model. Key factors considered include existing operator information and population density. The methodology integrates data cleaning, feature engineering, and model training, with validation performed using Power BI. Results demonstrate an accuracy of 84.62% for the Random Forest model, highlighting the importance of geographical location and population density in site suitability predictions. This approach will streamline the site selection process and enhance service quality, positioning operators as leaders in underserved areas. © 2025 IEEE.

关键词： Random forests

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Behind the Scenes: An Explainable Artificial Intelligence (XAI) on the Service Classification of the 5G/B5G Network 3

Behind the Scenes: An Explainable Artificial Intelligence (X...

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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Allias, Noormadinah Kadir, Diyana Ab Abdullahi, Akibu Mahmoud Ismail, Shahrinaz Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent. © 2024 IEEE.

关键词： Decision trees

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2025年第16期84卷 16971-17020页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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