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

作者： Murari, Krishn Balaji, N.V. Hemalatha, S. Bagade, Jayashree V. Kannagi, A. Sakhare, Nitin N. Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

this examines investigates the impact of different algorithmic methodologies on improving the overall performance of genetic algorithms. A complete evaluate of the literature became carried out including both theoretical and empirical investigations. A complete of fourteen studies has been covered, with ten being theoretical and four being empirical in nature. The methodologies studied on this synthesis covered the use of operators inclusive of mutation, crossover, and choice, as well as strategies consisting of elitism, micro-mutation, and mimetic algorithms. Throughout all fourteen studies, the outcomes confirmed that the use of those methodologies can have a fine impact on enhancing the performance of a given genetic set of rules. However, the value of the development turned into found to vary considerably between the one of a kind methodologies and genetic set of rules structures. Therefore, it seems that a particular methodology ought to be cautiously chosen primarily based at the characteristics of the given problem domain. Additionally, similarly research should cognizance on exploring approaches to optimize the combination of various methodologies for stepped forward performance. © 2023 IEEE.

关键词： empirical examine methodologies operators performance

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Measuring the Impact of Machine Learning on Real-Time Data Analysis 3

Measuring the Impact of Machine Learning on Real-Time Data A...

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

作者： Rengarajan, A. Kumar, Rohit Sakhare, Nitin N. Thangarasu, N. Mahalakshmi, V. Bhimanpallewar, Ratnmala N. Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350319125

Machine studying is unexpectedly revolutionizing the manner facts is analyzed and utilized in actual-time selections. with the aid of utilising effective algorithms, gadget gaining knowledge of can unlock effective insights from actual-time input statistics greater speedy and as it should be than traditional strategies. however, to make sure its fulfillment and effectiveness, there have to be a quantitative size procedure to assess how device studying algorithms are impacting the effectiveness of actual-time information evaluation. This paper pursuits to provide a comprehensive evaluation of the cutting-edge nation of device learning and its effect on actual-time facts analysis, as well as unique techniques to degree the effectiveness of device learning algorithms. specifically, the paper will observe the use of supervised studying fashions, unsupervised learning fashions, and reinforcement mastering strategies to create efficient and correct fashions that could quickly analyze and produce beneficial insights from real-time statistics. Additionally, this paper will talk various techniques that can be used to quantify the effectiveness of device gaining knowledge of fashions which includes predictive accuracy, records and function selection, and model interpretability. finally, the paper can even discuss challenges and destiny issues for measuring the authentic effect of machine studying on actual-time records analysis. © 2023 IEEE.

关键词： Learning algorithms

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An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique

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Biomedical Materials and Devices 2025年 1-15页

作者： Akter, Maksuda Khatun, Rabea Talukder, Md. Alamin Islam, Md. Manowarul Uddin, Md. Ashraf Ahamed, Md. Khabir Uddin Khraisat, Ansam Department of Computer Science and Engineering Jagannath University Dhaka India Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Crown Institute of Higher Education 116 Pacific Highway North Sydney 2060 NSW Australia Department of Computer Science and Engineering Bangamata Sheikh Fojilatunnesa Mujib Science and Technology University Jamalpur Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia

Skin cancer is a potentially life-threatening disease resulting from DNA damage, where early detection is critical to improving survival rates. This study introduces an advanced hybrid deep learning framework designed to enhance the accuracy of skin cancer classification, distinguishing between benign and malignant lesions. Our approach begins with data pre-processing to improve input quality, followed by training two high-performing pre-trained deep learning models, InceptionV3 and DenseNet121. We then apply a weighted sum rule for fusion of the model predictions, leading to high accuracy and generalizability across datasets. On the primary dataset, our hybrid model achieved 92.27% accuracy, 90.80% precision, 92.33% sensitivity, 92.22% specificity, and a 91.57% F1 score. Notably, the model also demonstrated robustness on an external dataset, achieving 93.45% accuracy, 91.87% precision, 93.04% sensitivity, 93.18% specificity, and a 92.30% F1 score, outperforming existing models. This study offers a reliable, highly generalizable solution for automated skin cancer diagnosis and represents a valuable contribution to early detection methods, with the potential to improve patient outcomes significantly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Densenet121 Feature fusion Hybrid deep learning InceptionV3 Skin cancer Transfer learning

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Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

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Application of improved virtual sample and sparse representation in face recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily *** paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert *** proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,*** proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and *** key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and *** proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and *** comparing with existing technologies,the proposed algorithm gave a better error rate and less *** rate and time is decreased by 19.79%,5.13 *** the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT *** proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks 25

Dual-level Mixup for Graph Few-shot Learning with Fewer Task...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400712746

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation 9th

Multi-Self-Supervised Light Graph Convolution Network for S...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhou, Yunsheng Zhou, Yinying Yu, Dunhui College of Computer and Information Engineering Hubei University Wuhan430062 China Education Informationization Engineering and Technology Center of Hubei Province Wuhan430000 China

ISBN: (纸本)9783031466731

Self-supervised learning is a novel paradigm between unsupervised and supervised learning, which can use the ground-truth samples automatically generated from the raw data for self-identification based contrastive learning and effectively alleviate the problem of data sparsity. Its ingenious combination with graph convolution network is a major breakthrough in the current recommendation field. However, mostly existing methods fail to fully consider the interference factors in social relations, fail to effectively improve the deviation of predicting scores only relying on the user and item representations, and fail to take into account the time cost of model training while improving the recommendation performance. To address these issues, this paper proposes a social recommendation framework based on multi-self-supervised light graph convolution network, named as MSR. Technically, MSR first utilizes the user-item sampling interaction graph to enhance the learning of user/item interaction representations, and then further enhances the learning of user social representations by fusing social relations through two-stage encoding. Finally, the interaction representations, social representations, and item bias are combined to predict scores. Our experiments on two real-world datasets LastFM and Douban-Book demonstrate that, compared with the current mainstream models, MSR improves the Precision@10 at least 2.35% and 10.41%, respectively, and reduces the time consumption at least 35.35% and 76.48%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolution

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Automated MOM Generator with Attendance Monitoring 7

Automated MOM Generator with Attendance Monitoring

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dharmale, Gulbakshee Deoghare, Radha Devkar, Anita Ganguly, Tanaya Deshmukh, Sphurti Pimpri Chinchwad College of Engineering Department of Information Technology Pune India Koneru Lakshmaiah Education Foundation Department Computer Science Engineering Guntur India

ISBN: (纸本)9798350304268

To automate the process of scripting the minutes of meetings and monitoring the attendance of meeting attendees. Minutes of the meeting contains revised information about various decisions taken, discussions done on many agendas, the final abstract of the meeting and the attendance of attendee's present in the meeting. Generating the Meeting minutes document is a manual and time-consuming process. We are proposing a solution to automate the process of scripting minutes of meetings which is currently done manually and keep track of attendees of meetings. This would reduce the effort of generating minutes document manually and distributing it among attendees of meeting. This project is divided into 3 modules the first module would be about converting meeting recorded audio to text, the second module would focus on text summarization which would include objectives, agendas, date and time of the meeting, decisions, and issues discussed in meeting, and the third module would focus on getting the attendance of meetings participants. Our proposed hardware device would be able to capture long range voice as it would be using sound shark mic, after which it would process and convert this recorded meeting audio to text and summary of this text would be generated with inbuilt machine learning model with help of embedded chip. This hardware device would contain a camera to capture faces of meeting attendees which would be passed to face recognition model for monitoring the attendance of meeting attendees. After the meeting MOM document would be generated and sent via mail to admin/host. © 2023 IEEE.

关键词： Automation Deep Learning NLTK Speech-to-text Text Summarization VHOSK

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