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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Machine Learning for Cardiac Disease Detection and Family History Association 3

Machine Learning for Cardiac Disease Detection and Family Hi...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Srinivas, Inumarthi V. Saritha, G. Yadav, Renu Subburam, S. Divya, K. Kj Somaiya Institute of Management Data Science and Technology Depatment Mumbai India Sri Sairam Institute of Technology Computer Science India Ies College of Technology Department of Electronics & Communication Engineering Bhopal India New Prince Shri Bhavani College of Engineering and Technology India Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350342338

As cardiac diseases continue to rise, early detection is crucial for effective treatment. Machine learning (ML) offers promising avenues for accurate diagnosis. This study explores the integration of family history, a vital risk factor, into ML-based cardiac disease detection models. By leveraging diverse patient datasets, ML algorithms are trained and validated. Incorporating family history enhances predictive accuracy. The literature review highlights the significance of genetics in disease predisposition. Model performance metrics demonstrate improved precision, recall, and overall accuracy. Interpretation of results reveals the interplay between genetics and ML. While data limitations and biases are acknowledged, the study presents implications for both clinical practice and research. This research contributes to the growing field of ML-driven medical diagnostics and underscores the importance of family history in cardiac disease prediction. © 2023 IEEE.

关键词： Clinical decision support disease prediction and machine learning genetic predisposition healthcare analytics healthcare applications patient datasets

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Privacy Preserving Discretized Spiking Neural Network with TFHE

Privacy Preserving Discretized Spiking Neural Network with T...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Li, Pengbo Gao, Ting Huang, Huifang Cheng, Jiani Gao, Shuhong Zeng, Zhigang Duan, Jinqiao Huazhong University of Science and Technology Center for Mathematical Sciences Wuhan China Huazhong University of Science and Technology School of Mathematics and Statistics Wuhan China Clemson University School of Mathematical and Statistical Sciences Clemson United States Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Great Bay University The Dongguan Key Laboratory for Data Science and Intelligent Medicine Depatment of Mathematics and Department of Physics Dongguan China

ISBN: (纸本)9798350316889

Artificial intelligence not only offers significant convenience but also raises privacy and security concerns, which pose challenges for Spiking Neural Networks (SNNs). Fully Homomorphic Encryption (FHE) is a crucial privacy-preserving computation technology, and the discrete nature of SNNs naturally aligns with FHE's requirements. In this paper, we construct a framework to evaluate the homomorphic computation of SNN named FHE-DiSNN that enables SNN to achieve good prediction performance on encrypted data. First, benefitting from the discrete nature of spike signals, our proposed model avoids the errors introduced by discretizing activation functions. Second, by applying bootstrapping, we design new privacy-preserving functions FHE-Fire and FHE-Reset, through which noise can be refreshed, allowing us to evaluate SNN for an arbitrary number of operations. Furthermore, we improve the computational efficiency of FHE-DiSNN while maintaining a high level of accuracy. Finally, we evaluate our model on the MNIST dataset. The experiments show that FHE-DiSNN with 30 neurons in the hidden layer achieves a minimum prediction accuracy of 94.4%. Under optimal parameters, it achieves a 95.1% accuracy, with only a 0.6% decrease compared to the original SNN (95.7%). These results demonstrate the superiority of SNN over second-generation neural networks for homomorphic evaluation. © 2023 IEEE.

关键词： Computational efficiency

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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review

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Oral Oncology Reports 2024年 9卷 100217-100217页

作者： Singh, Viomesh Sultanpure, Kavita A. Patil, Harshwardhan Department of Artificial Intelligence and Data Science Vishwakarma Institute of technology Pune India Department of information Technology Vishwakarma Institute of technology Pune India Depatment of Information Technolgy Vishwakarma Institute of Technology Pune India

Skin cancer stands as one of the prevalent and life-threatening malignancies, witnessing a substantial global surge in reported cases. Failure to diagnose it at its incipient stages may lead to metastasis, significantly elevating mortality rates. Early detection, however, presents a curative prospect. Thus, the prompt and precise diagnosis of skin cancers remains a paramount focus in current research. Numerous machine learning techniques have been seamlessly woven into the fascinating world of computer-aided skin cancer diagnosis and figuring out if that blemish is a real troublemaker. Machine learning is like the Sherlock Holmes of artificial intelligence. It's got these brainy models and algorithms that not only soak up information but also play psychic by predicting stuff on brand new data it's never laid eyes on before. In contrast to the conventional biopsy method, which is both laborious and costly, machine learning algorithms offer a viable alternative for early detection, reducing the burden on specialists while concurrently augmenting the diagnostic accuracy of skin lesions. In this article we delve into a thorough exploration of cutting-edge machine learning methods that play a crucial role in identifying those sneaky signs of skin cancer. I took a deep dive into the pool of relevant studies, offering you an insider's look into the performance of the friendly neighborhood the k-nearest neighbor approach, the robust SVM, and the sophisticated CNN. In the review we Openly discussed the inherent limitations and quirks of each algorithm, affording them the spotlight they deserve. © 2024 The Author

关键词： Artificial Intelligence Machine learning Melanoma Skin cancer

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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Epidermis Bruise Analysis Using Deep Learning

Epidermis Bruise Analysis Using Deep Learning

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Vansheta Sharma Rupa Rani Priya Pathak Sudeshna Chakraborty Department of Computer Science & Engineering- Data Science ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan Department School of Computer Application and Technology Galgotias University UP India

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

关键词： Deep learning Analytical models Accuracy Biological system modeling Computer architecture Feature extraction Skin

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Dualband and Tripleband Metamaterial Absorber in Wr1 Band and Lower Terahertz Frequencies

SSRN

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SSRN 2023年

作者： Gharat, Sarvesh Kulkarni, Prutha Prabhu, Shriganesh Garde, Chandrashekhar Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Electronics and Telecommunication Vishwakarma Institute of Information Technology Pune India Depatment of Engineering and Applied Sciences Vishwakarma Institute of Information Technology Pune India Department of Condensed Matter Physics and Materials Science Tata Institute of Fundamental Research Mumbai India

The frequency Band of 0.5 THz to 1.1 THz (WR1.5 and WR1) is one of the promising bands when it comes to 6G. In this paper, we propose a novel metamaterial absorber suitable to be used at lower WR1 frequencies in TE mode. Alternatively, the design can be used as a strong absorber at lower Terahertz frequencies. In addition, when used in TM mode the absorber works as a perfect absorber at a higher WR1 band. © 2023, The Authors. All rights reserved.

关键词： Metamaterials

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Machine Learning for Cardiac Disease Detection and Family History Association

Machine Learning for Cardiac Disease Detection and Family Hi...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Inumarthi V. Srinivas G. Saritha Renu Yadav S. Subburam K. Divya Data Science and Technology Depatment KJ Somaiya Institute of Management Mumbai India Computer Science Sri Sairam Institute of Technology India Department of Electronics & Communication Engineering IES College of Technology Bhopal India New Prince Shri Bhavani College of Engineering and Technology India Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

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Privacy Preserving Discretized Spiking Neural Network with TFHE

Privacy Preserving Discretized Spiking Neural Network with T...

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Neuromorphic Computing (ICNC), International Conference on

作者： Pengbo Li Ting Gao Huifang Huang Jiani Cheng Shuhong Gao Zhigang Zeng Jinqiao Duan Center for Mathematical Sciences Huazhong University of Science and Technology Wuhan China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Mathematical and Statistical Sciences Clemson University Clemson U.S.A School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Depatment of Mathematics and Department of Physics The Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay University Dongguan China

Artificial intelligence not only offers significant convenience but also raises privacy and security concerns, which pose challenges for Spiking Neural Networks (SNNs). Fully Homomorphic Encryption (FHE) is a crucial privacy-preserving computation technology, and the discrete nature of SNNs naturally aligns with FHE’s requirements. In this paper, we construct a framework to evaluate the homomorphic computation of SNN named FHE-DiSNN that enables SNN to achieve good prediction performance on encrypted data. First, benefitting from the discrete nature of spike signals, our proposed model avoids the errors introduced by discretizing activation functions. Second, by applying bootstrapping, we design new privacy-preserving functions FHE-Fire and FHE-Reset, through which noise can be refreshed, allowing us to evaluate SNN for an arbitrary number of operations. Furthermore, we improve the computational efficiency of FHE-DiSNN while maintaining a high level of accuracy. Finally, we evaluate our model on the MNIST dataset. The experiments show that FHE-DiSNN with 30 neurons in the hidden layer achieves a minimum prediction accuracy of 94.4%. Under optimal parameters, it achieves a 95.1% accuracy, with only a 0.6% decrease compared to the original SNN (95.7%). These results demonstrate the superiority of SNN over second-generation neural networks for homomorphic evaluation.

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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

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