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Smart Energy Management System Using Machine Learning

computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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A Transfer Learning Based Detection and Grading of Cataract using Fundus Images 25

A Transfer Learning Based Detection and Grading of Cataract ...

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25th International Multi Topic Conference, INMIC 2023

作者： Abbas, Ahsan Alzahrani, Abdulkareem Imran, Azhar Almuhaimeed, Abdullah Khan, Ali Haider Air University Department of Creative Technologies Islamabad Pakistan Al-Baha University Faculty of Computer Science & It Saudi Arabia Digital Health Institute King Abdulaziz for Science and Technology Riyadh Saudi Arabia Lahore Garrison University Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350317701

Among the most prominent causes for visual impairment, especially among older adults, is cataract. As per the World Health Organisation (WHO), there are 2.2 billion individuals globally are estimated to be blind or possess an eyesight problem. One of the most identified and important causes of this is cataracts. Cataracts should be identified and treated as soon as possible to avoid blindness. Ophthalmologists use an expensive slit lamp to diagnose cataracts in regions with few medical facilities. Consequently, the issue is that a lack of skilled ophthalmologists may delay the identification of cataracts, for which medical treatment is unavoidable. Medical image analysis based on artificial intelligence provides a rapid and precise diagnosis in modern healthcare. We utilized deep learning models based on transfer learning, namely VGG19, and ResNet-50, to diagnose cataracts using fundus images and enhance classification accuracy. The performance metric for the model is accuracy;the highest achieved accuracy was 98%. © 2023 IEEE.

关键词： Diagnosis

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Faceswap Finder: A Fusion-Based Deepfake Detection Technique

Faceswap Finder: A Fusion-Based Deepfake Detection Technique

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Nimra Basit Fatima Khalid Qurat Ul Ain Maria Andleeb Department of Software Engineering University of Engineering and Technology Taxila Pakistan Faculty of Computer Sciences and Engineeing GIKI Topi Pakistan Department of Computing Shifa Tameer-e-Millat University PRC Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The significant improvement in deepfake generation algorithms has made possible the manipulation of visual data. The advancement of GAN technology and tools has made it relatively simple to use source and target photos and build realistic deepfakes. The challenges associated with deepfakes include political defamation, impersonation, misinformation, and cyberstalking. The generation of deepfakes through faceswap approach hinders the detection oftentimes as it spans numerous artifacts – illumination, facial skin tones, and other conditions. Existing techniques do not employ robust mechanisms to detect such deepfakes either due to computational inefficiency or focusing on a few parameters for decision-making. To counteract these dangers, a robust deepfake detection model must be developed and implemented. This paper proposes Efficient-Fused Net (EF-Net) - a transfer learning approach to two EfficientNet-B5 models with partial freezing between its layers that can differentiate between real and fake images generated from faceswap implementation. We evaluated our model’s performance using the challenging deepfake subset of the extensive and varied FaceForensics++ and DFDC-Preview datasets. The proposed method outperforms state of the art methods in the detection of faceswap deepfakes, surpassing the performance by $89.19 \%$ accuracy on DFDC-Preview and $\mathbf{9 7 . 7 0 \%}$ of Deepfakes subset of FaceForensics++.

关键词： Deepfakes Visualization Computational modeling Transfer learning Decision making Lighting Focusing Skin Robustness Resilience

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Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ...

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Multimedia University engineering Conference (MECON)

作者： Muhammad Salman Qamar Ihsan ul Haq Muhammad Fahad Munir Mardeni Bin Roslee Adnan Anwar Awan Athar Waseem Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Centre for Wireless Technology Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Computer Science and Software Engineering School of Engineering California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN.

关键词： Wireless sensor networks Energy consumption Simulation Artificial neural networks Data aggregation Energy efficiency Magnetic heads Routing protocols Batteries Optimization

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Practical Secure Heterogeneous Communication for Internet of Things

SSRN

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SSRN 2022年

作者： Jin, Chunhua Zhu, Huihui Qin, Wenyu Chen, Zhiwei Jin, Ying Shan, Jinsong Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an233003 China

Internet of things (IoT) is a promising network that combines various information sensing devices with the Internet to realize the interconnection of pervasive things. Security is a pivotal issue to ensure widespread adoption of the IoT. To ensure the security of data transmission, in this paper, we construct a heterogeneous online and offline signcryption from a wireless sensor network (WSN) to an Internet server. In our construction, the WSN is in certificateless cryptosystem (CLC) which does not involve public key certificate management problem in public key infrastructure (PKI) and key escrow problem in identity based cryptosystem (IBC), while the server is in IBC which avoids public key certificate management problem. For signcryption process, the proposed scheme is split into two steps: offline step and online step. In the former step, the time-consuming calculations are completed, but do not involve the transmitted message. In the latter step, only some lightweight calculations are involved, but message must be available. Confidentiality, authentication, non-repudiation, and integrity can be achieved in a logical single step. In addition, an elaborate performance analysis demonstrates that our proposed scheme has advantages in terms of computational cost and private key size, offline storage, as well as communication overhead. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Navigating the future of higher education in Saudi Arabia: implementing AI, machine learning, and big data for sustainable university development

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Discover Sustainability 2025年第1期6卷 1-17页

作者： Khan, Muhammad Adnan Rehman, Abdur Shah, Asghar Ali Abbas, Sagheer Alharbi, Meshal Ahmad, Munir Ghazal, Taher M. School of Computing Skyline University College Sharjah 1797 United Arab Emirates Riphah School of Computing & Innovation Faculty of Computing Riphah International University Lahore Campus Lahore 54000 Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-Si 13557 South Korea Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Sciences National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science Kateb University Kabul Afghanistan Department of Computer Science Prince Mohammad Bin Fahd University Dhahran Al Khobar 34754 Saudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia University College Korea University Seoul 02841 South Korea Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan

Higher education in the Gulf Cooperation Council (GCC) is going through big changes as universities try to meet the needs of 21st-century students and society. New technologies give both opportunities and challenges for Arab region universities to develop sustainably. This paper looks at ways to successfully use artificial intelligence (AI) and big data analytics in Saudi higher education while supporting long-term growth. First, it analyzes current trends in Saudi university enrollment, programs, and facilities to identify areas for improvement. It then explores the potential benefits of AI and big data, like personalized learning, better campus operations, and data-driven decision-making. However, there are also risks like high costs, privacy concerns, and lack of qualified people that need to be addressed. Recommendations are given for overcoming barriers to adopting these technologies including getting stakeholders involved, developing customized AI solutions, and starting tech-focused academic programs. The paper also discusses long-term impacts on faculty roles, student experiences, and financial sustainability. In the end, carefully implemented AI and big data can improve learning, and student services, and cut costs but require careful change management. By balancing cutting-edge tech with local needs, GCC universities can provide innovative education while upholding traditions and values. This study explores how AI, Machine Learning, and Big Data can enhance sustainability and effectiveness in Saudi higher education, aligning with relevant UN Sustainable Development Goals. Results highlight AI-driven insights that improve institutional decision-making and educational equity. Results indicate that among the predictive models tested, Random Forest achieved the highest accuracy in student performance prediction, with an R² score of 0.85. © The Author(s) 2025.

关键词： Artificial intelligence Big data Educational policy Higher education Sustainability

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Image Classification Analysis to Identify the Tourism Potential Sukabumi on Instagram Using A Deep Learning Approach

Image Classification Analysis to Identify the Tourism Potent...

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International Conference on Informatics and Computing (ICIC)

作者： Falentino Sembiring Rieska Rahayu Ayuningsih Muhamad Muslih Muhammad Andy Maulana Gina Wahdayani Futri Mohd Zainuri Saringat Rachma Siva Ainunnisa Anggun Fergina Information Systems Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This research focuses on image classification, grouping images based on shared characteristics. The study aims to identify the highest-potential category within various fields, from high to low potential. By understanding existing potential, researchers can inform targeted strategies for future development. The methodology combines Logistic Regression in Orange Data Mining to enhance classification accuracy. Hierarchical Clustering is used to analyze 7,777 image results. Findings reveal that Tourism (C3) has the most potential, followed by Culinary (C2) and Culture (C4), while Handcrafts (C1) exhibit the least potential. In conclusion, logistic regression successfully identifies influential aspects of Sukabumi through Instagram images, aiding potential development

关键词： Deep learning Logistic regression Accuracy Web sites Multimedia communication Data mining Cultural differences Informatics Image classification

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Dang Van Anh Abdellah Chehri Nguyen Minh Quy Chu Thi Minh Hue Dinh C. Nguyen Vu Khanh Quy Faculty of Information Technology Hung Yen University of Technology and Education Vietnam Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Faculty of Software Engineering FPT University Hanoi Vietnam Department of Electrical and Computer Engineering The University of Alabama in Huntsville Alabama USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems’ service response times across various workloads and traffic.

关键词： Cloud computing Collaboration Internet of Medical Things Load management Real-time systems Servers Time factors software defined networking Optimization Edge computing

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Toward Robust Learning via Core Feature-Aware Adversarial Training

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6236-6251页

作者： Fengpeng Li Kemou Li Haiwei Wu Jinyu Tian Jiantao Zhou Department of Computer and Information Science Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Taipa Macau China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China

Deep neural networks (DNNs) are inherently vulnerable to adversarial examples (AEs), severely deteriorating model performance on various tasks. Adversarial training (AT) is one of the most effective approaches to enhance model robustness by incorporating AEs into the training process. Notwithstanding the efficacy of AT, recent studies have unveiled that adversarial perturbations on AEs predominantly impact core features—essential for accurate predictions—more than spurious features, which are incidentally aligned with training labels but irrelevant to the model’s classification. This unequal impact induces the models trained with AT to excessively rely on spurious features, resulting in a pronounced feature shift that compromises robustness and generalization against AEs at inference. In this work, we introduce a novel Core Feature-aware Adversarial Training ( CoFAT) framework to cope with these challenges. CoFAT employs core feature extraction to dynamically generate core partners by selectively retaining benign sample regions on feature maps with high-weight while masking low-weight ones, thereby ensuring the model focuses on core features. Furthermore, contrastive feature alignment is proposed to reduce intra-class feature distances and increase inter-class separability by maintaining a center bank of class feature representations, thus mitigating reliance on spurious features. Compared to state-of-the-art AT methods, CoFAT demonstrates superior performance against diverse adversarial attacks. Remarkably, CoFAT improves the robustness of ResNet-18 against AutoAttack on CIFAR-10, SVHN, CIFAR-100, and Tiny ImageNet by approximately 2.14%, 3.20%, 1.69%, and 1.86%, respectively, embodying significant advancements in AT. Our code is publicized at https://***/Feng-peng-Li/CoFAT

关键词： Robustness Feature extraction Training Perturbation methods Predictive models Cows Buildings Accuracy Representation learning Optimization

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