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OCS-Net: a novel approach for oral carcinoma stage classification using generative adversarial networks and genetic algorithm-based convolutional neural networks

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International Journal of information technology (Singapore) 2023年第8期15卷 4097-4110页

作者： Agarwal, Pinky Yadav, Anju Mathur, Pratistha Department of Computer Application Manipal University Jaipur Rajasthan Jaipur 303007 India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur 303007 India

The World Health Organization's 2022 report states that oral cancer affects 3.5 billion people worldwide and is responsible for an expected 177,757 fatalities in 2020. Oral cancer is hazardous because it is difficult to detect in its early stages. To solve this problem, our research introduces the ground-breaking deep learning framework OCS-Net. The framework's goal is to categorize the different stages of oral cancer utilizing CT scan pictures from S.M.S. Hospital in Jaipur patients. We ran into the issue of class imbalance in the dataset during the data collection procedure. To solve this problem, we used generative adversarial networks to create artificial images that represented all classes fairly. Additionally, we modified the convolutional neural network parameters used in OCS-Net using genetic algorithm methods. The OCS-Net evaluation produced remarkable results with an AUC-Score of 98.07% and an average accuracy of 98.05%. In terms of performance and accuracy, OCS-Net outperformed well-known transfer learning models including VGG-16, VGG-19, ResNet-101, and MobileNet. This study underlines the value of applying deep learning approaches to medical image processing while also showcasing the potential of OCS-Net as a useful tool for precise oral cancer stage evaluation. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Convolution neural network (CNN) Generative adversarial network (GAN) Genetic algorithm (GA) Oral cancer stage classification

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Hybrid Electroencephalography-Induced Robot Navigation for Rehabilitative Platform 20

Hybrid Electroencephalography-Induced Robot Navigation for R...

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20th IEEE India Council International Conference, INDICON 2023

作者： Ghosh, Ahona Ghosh, Lidia Dey, Sharmistha Sarkar, Indranil Saha, Sriparna Maulana Abul Kalam Azad University of Technology Department of Computer Science & Engineering West Bengal India Rcc Institute of Information Technology Department of Computer Application Kolkata India University Institute of Computing Chandigarh University Punjab Gharuan India Guru Nanak Institute of Technology Department of Computer Application India

ISBN: (纸本)9798350305593

Decoding upper limb movement from electroencephalography (EEG) signals is an emerging field of research that has the potential to enhance the quality of life for individuals with motor impairments. This paper provides an informative overview of EEG-based upper limb movement decoding and addresses the limitations found in existing studies by proposing a novel rehabilitative robotics framework. The framework utilizes three types of EEG signals: motor imagery, error-related potential, and P300 signals, to navigate an OpenManipulatorX robotic arm. The novelty of this research lies in the utilization of a phase-sensitive Common Spatial Pattern approach for feature extraction, as well as the successful recognition of commands for robot control using Linear Discriminant Analysis. The proposed method achieves an impressive average success rate of 95% for target reaching, making it suitable for the design of prosthetics in real-time rehabilitative platforms. © 2023 IEEE.

关键词： Electroencephalography Linear Discriminant Analysis OpenManipulatorX Phase-sensitive Common Spatial Pattern Rehabilitation

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Examining the application of Machine Learning to Improve Organizational Performance in the Medical Setting 15

Examining the Application of Machine Learning to Improve Org...

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

作者： Shelke, Ganesh C. Dutta, Abhishek Katariya, Jitendra Kumar Sakhare, Nitin N. Anandhasilambarasan, D. Mahalakshmi, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350370249

This paper examines the capacity utility of machine gaining knowledge of within the clinical putting to enhance organizational overall performance and affected person effects. The paper begins by means of offering a top level view of ways gadget gaining knowledge of is being utilized in other industries and the way it is able to be applied inside the scientific placing. It then goes on to speak about the important thing challenges related to growing and deploying gadget learning inside the clinical putting which include statistics privateness and security, records availability, and they want for comprehensive expertise of clinical contexts. It examines the modern-day literature on machine studying inside the scientific placing to perceive satisfactory practices and proposes regions of destiny studies. In the end, the paper summarizes the implications from the studies, highlighting the capacity advantages and demanding situations that want to be addressed earlier than machine gaining knowledge of may be efficiently implemented in the medical field. Universal, this paper offers an informative evaluation of the capability programs of machine gaining knowledge of inside the clinical placing and emphasizes the need for greater studies in this place. © 2024 IEEE.

关键词： clinical gaining greater satisfactory situations

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IoT Based Smart Systems using Artificial Intelligence and Machine Learning: Accessible and Intelligent Solutions 6

IoT Based Smart Systems using Artificial Intelligence and Ma...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Ahmed, Bilal Shuja, Mirza Mishra, Hari Mohan Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Punjab Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

The Internet of Things (IoT) has grown in importance in both the technological and social spheres as consumers want smarter homes, more efficient businesses, and more efficient healthcare systems. The social setting is very important for this. Communication networks provide uninterrupted internet connectivity for both fixed and mobile devices like PCs and smartphones. Customers may now keep up their level of engagement while interacting with one or more apps at any time and from any location, thanks to developments in information and communication technologies. Many kinds of smart devices might potentially coordinate their responses to events, sending out alerts or keeping records of the system's condition in line with a predetermined policy. Constant progress toward intelligent solutions is improving many facets of modern life, including manufacturing, transportation, electronic healthcare, electronic education, and many more. Machine-to-machine (M2M) communication in the IoT is autonomous and dynamic. Diverse approaches are being developed to address the challenges posed by disparate data sources, fluctuating signal quality, and overwhelming quantities of data. From the perspectives of intelligent system technologies, security, vulnerabilities, and the potential role that intelligent solutions based on Machine Learning (ML) and Artificial Intelligence (AI) may play, this article examines the IoT. Concerns about privacy and safety have arisen as a major obstacle to the widespread adoption of the Internet of Things (IoT). We propose that the Standard Security Framework (SSF) be made mandatory for all distributed networks to ensure that hardware and software modules may communicate with one another regardless of their respective platforms. In addition, the effectiveness and security of new innovations in using fog and edge computing in conjunction with cloud applications have been studied.. © 2023 IEEE.

关键词： Internet of things

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A Study on the Design of Eye and Eyeball Method Based on MTCNN 1

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13th International Conference on Frontier Computing, FC 2023

作者： Hsueh, Cheng-Yu Hung, Jason C. Tzeng, Jian-Wei Huang, Hui-Chun Huang, Chun-Hong Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404348 Taiwan Department of Information Management National Taichung University of Science and Technology Taichung404348 Taiwan Department of Innovation Application and Management Chang Jung Christian University Tainan71101 Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan33306 Taiwan

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Studies on eye tracking have relied on wearable eye trackers and chin-resting eye trackers, but the high cost of equipment and the need to wear devices during experiments can lead to less natural facial movement. This study collected eye-tracking data by using a raw-video camera without adjusting any parameters. Python was used as the primary programming language. Eye tracking was adjusted through calculations of facial distance, and multitask cascaded convolutional networks were used to collect eye-tracking data. The corrected results were visualized, and linear regression was used to determine correction error. The root mean square error was 221.66, and the mean squared error was 260.48. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Eye tracking

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Enhancing Speech Emotion Recognition Combining Silence Elimination and Attention Model with a Novel CNN Architecture 8th

Enhancing Speech Emotion Recognition Combining Silence Elimi...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Gawali, Piyush P. Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

A natural interface for human–computer interaction is automatic speech emotion recognition, but it encounters challenges in handling non-emotional speech segments, especially silence, since it is non-emotional speech segmentation that requires recognition. Using a carefully designed attention model and a silence elimination approach, this paper proposes a novel method of enhancing emotion recognition performance by combining silence elimination with attention. In the present study, an enhanced Convolutional Neural Network architecture has been proposed, comprising of nine layers, utilizing a combination of convolutional 1D As a result of the research, CNNs have been employed in a revolutionary speech-emotion detection system that reaches an impressive 96.00% accuracy rate. There is great potential in this breakthrough to develop social robots and conversational robots capable of conveying nuanced human emotions. This breakthrough has marked a significant improvement in accuracy compared to prior models based on the same dataset. In addition to the broader classifications of positive, negative, and neutral emotions, the model's accuracy is evaluated across a wide range of emotion classes, such as anger, calmness, fear, happiness, and sadness for both genders. There is considerable evidence to suggest that combined noise cancellation and attention models are more effective than individual models of noise cancellation or attention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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