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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Gaganjot Kaur, Shinepreet Devi, Shivali Chandigarh University Department of Computer Science and Engineering Mohali India Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Recognizing a face is an intricate cognitive process that showcases the remarkable capabilities of the human brain in visual perception, a phenomenon deeply rooted in evolutionary biology. In an attempt to emulate this complex phenomenon, computer vision technology has been developed to serve as a sophisticated tool capable of recognizing and distinguishing faces, akin to the human visual system, particularly in the domain of biometric identification methods employed for verifying individuals' identities. This is done by identifying the unique features of a human's face through various algorithms. This paper reviews the efficacy of popular approaches to portraying facial features in face pictures. Geometrical-based methods, template-based methods, and appearance-based methods were traditional ways for extracting facial features before the onset of Machine learning for this application. Accuracy through confusion matrix have been comprehended and traditional methods have been found more fit for static and atomic systems although, machine learning algorithms give better and consistent results at the cost of developing and maintaining the computational system underneath. © 2024 IEEE.

关键词： 468 landmark detection discrete wavelet transform eigen faces Facial features detection geodesic distance local binary pattern morphometry

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Green hydrogen production from photovoltaic power station as a road map to climate change mitigation

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Clean Energy 2024年第2期8卷 156-167页

作者： Kehinde Ridwan Kamil Bassey Okon Samuel Umar Khan Department of Computer Science and Engineering Edge Hill UniversityUK Enel Green Power Rome Italy Materials Research Laboratory Steelagon Engineering LimitedKaduna StateNigeria

The increasing recognition of hydrogen as a critical element in the global net-zero transition and its clear role in decarbonizing challenging sectors coincide with the growing urgency to address climate ***'s favourable renewable-energy capacity,ranging from 28%to 36%for solar,has been reported by the global solar irradiance ***,the majority of hydrogen production today relies on fossil fuels(96%),with only a small fraction(4%)being produced through water *** though there have been many studies on climate change mitigation with a focus on Africa,a green hydrogen production from a photovoltaic power station approach has not been ***,literature with a focus on Nigeria is *** study focuses on the African green hydrogen production industry,utilizing Nigeria as a case study to explore the feasibility of generating clean hydrogen vectors from a percentage of photovoltaic power output in various regions of the country through stand-alone solar grid electrification *** of the usage and effectiveness of the produced hydrogen fuel in each region are carried out,with the highest region having an annual output of 12247278 kg of green hydrogen and 8573094 kg of ammonia and the lowest region having an output of 511245 kg of green hydrogen and 357871 kg of ammonia,and the expected production from the proposed usage of 50%of the power generation output of the installed 1.6-MWp and 80-kWp solar power minigrids in the regions is *** analyses were repeated for the other considered regions in the *** results showcased the enormous advantages of the electrolytic production of hydrogen and how the greener economy project can play a major role in mitigating climate change effects and overreliance on fossil fuels as the driver of the economy in many African countries.

关键词： renewable energy photovoltaic climate change green hydrogen vector fossil fossil fuels decarbonization electrolysis

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D2LFS2Net:Multi-class skin lesion diagnosis using deep learning and variance-controlled Marine Predator optimisation:An application for precision medicine

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 207-222页

作者： Veena Dillshad Muhammad Attique Khan Muhammad Nazir Oumaima Saidani Nazik Alturki Seifedine Kadry Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable *** imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin *** optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion *** sequential procedures of the proposed system start with preprocessing and end with *** preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy *** of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next ***,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched ***,a dual-threshold serial approach is employed to obtain and combine the features of both *** next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation *** top features from the fused feature vector are classified using machine learning *** experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset ***,the proposed framework is evaluated compared to current approaches,with remarkable results.

关键词： contrast enhancement deep learning dermoscopic images features optimization fusion skin cancer

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Hybrid feature selection method for predicting software defect

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Journal of engineering and Applied science 2024年第1期71卷 124页

作者： Anju, A.J. Judith, J.E. Department of Computer Science Engineering Eranad Knowledge City Technical Campus Kerala Manjeri India Department of Computer Science Engineering Noorul Islam Center for Higher Education Tamil Nadu Kanyakumari India

To address the challenges associated with the abundance of features in software datasets, this study proposes a novel hybrid feature selection method that combines quantum particle swarm optimization (QPSO) and principal component analysis (PCA). The objective is to identify a subset of relevant features that can effectively contribute to the accuracy of a predictive model based on an artificial neural network (ANN). The quantum particle swarm optimization algorithm is employed to optimize the selection of features by simulating the behavior of quantum particles in a search space. This approach enhances the exploration and exploitation capabilities, allowing for a more effective identification of relevant features. Furthermore, principal component analysis is integrated into the hybrid method to reduce dimensionality and remove multicollinearity among features, thereby improving the efficiency of the feature selection process. The proposed hybrid method is applied to software defect datasets, where the selected subset of features is fed into an artificial neural network for defect prediction. The performance of the hybrid model is compared with traditional feature selection methods, standalone QPSO, and PCA. Experimental results demonstrate the effectiveness of the hybrid approach in achieving superior predictive accuracy while reducing the dimensionality of the dataset. The proposed approach not only enhances prediction accuracy but also provides a more interpretable and efficient subset of features for building robust defect prediction models. © The Author(s) 2024.

关键词： Feature Selection

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Robust tube-based MPC with smooth computation for dexterous robot manipulation

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science China(Information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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A New Alliance of Machine Learning and Quantum Computing: Concepts, Attacks, and Challenges in IoT Networks

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IEEE Internet of Things Journal 2025年第12期12卷 18865-18886页

作者： Rishiwal, Vinay Agarwal, Udit Yadav, Mano Tanwar, Sudeep Garg, Deepak Guizani, Mohsen MJP Rohilkhand University Department of Computer Science and Information Technology Bareilly India Bareilly College Bareilly Department of Computer Science India Nirma University Institute of Technology Department of Computer Science and Engineering Gujarat Ahmedabad382481 India SR University School of Computer Science and Artificial Intelligence Telangana- Warangal506371 India Abu Dhabi United Arab Emirates

The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge. © 2025 IEEE.

关键词： Quantum computers

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Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

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Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

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Performance Evaluation of a Multi-input Interleaved Boost Converter with a Tuned Proportional-integral Controller

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Chinese Journal of Electrical engineering 2024年第4期10卷 119-128页

作者： Sucharita Pal Dola Sinha Mou Das Mahapatra Saibal Majumder Sovan Bhattacharya Chan Byopadhyay Department of Electrical Engineering Asansol Engineering CollegeAsansol 713305India Department of Electrical Engineering Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering(Data Science) Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering University of BremenBremen 28359Germany

The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.

关键词： Multi-input converter interleaved boost converter pulse-width modulation proportional-integral controller

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Photovoltaic power one-day and multistep-hourly AI predictions using node-by-node evolved binomial tree structures to form L-transformed PDE modules

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Soft Computing 2025年第5期29卷 2483-2495页

作者： Zjavka, Ladislav Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 15/2172 Ostrava Czech Republic

Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead

关键词： Laplace transforms

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