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Information Technologies and Communications (GCITC), Global Conference on

作者： R Vijay Prakash Vinay Kumar Nassa Smrity Prasad Anupama Jha Patel Rasikkumar Dahyalal Radha Raman Chandan School of CS &AI SR University Warangal Telangana India Department of Information Communication Technology(ICT) Tecnia Institute of Advanced Studies(Delhi) Affiliated With Guru Gobind Singh Indraprastha University Computer Applications Dayananda Sagar University Bengaluru Karnataka VSIT VIPS GGSIP University Delhi Sankalchand Patel College Of Engineering Sankalchand Patel University Visnagar Gujarat Department of Computer Science School of Management Sciences(SMS) Varanasi U.P India

The security issues of data and information have grown extremely significant in the context of the accelerated growth of information technology. In everyday web browsing or interactions, information and data may be compromised. When sharing data, it is important to examine the security features of the secure data-sharing system. The purpose of this paper is to investigate the security mechanisms of cloud computing-oriented big data sharing systems in the Internet of Things age. This paper introduced the attribute-driven encryption (ADE) technique, provided an in-depth description of the ADE technique, and examined security issues in cloud computing big data sharing. According to the testing data, the ADE technique consumes a mean of 11 seconds with five attempts, while the additional 2 approaches use 51.8 & 31.6 seconds, respectively ADEs use lesser duration and are far more effective than the additional 2 techniques for various encryption values beneath identical data. As a result, ADE techniques must possess a competitive edge.

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Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

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Journal of Stroke Medicine 2022年第2期5卷 126 - 132页

作者： Girish Menon Hareesha KS Karthik Gajula Sampath Kumar Ajay Hegde Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics & Communications Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

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A Novel Machine Learning-Based Artificial Voice Box

A Novel Machine Learning-Based Artificial Voice Box

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Naveen Kumar Priya Nandihal Madhumala R B Piyush Kumar Pareek Nikshepa T Sowmya S R Department of Computer Applications (BCA) Dr NSAM First Grade College (Affiliated to Bangalore City University) Bengaluru Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

Patients may experience a great deal of discomfort while undergoing rigorous medical procedures for the identification of vocal abnormalities. As a result, there has been a lot of interest in automated speech recognition and disorder detection approaches in recent years, and these methods have shown to be effective. Voice recordings have been acquired from the Saarbruecken Voice Database for the purpose of this study. The signals undergo preprocessing using Hybrid Wiener Filter Discrete Wavelet Transforms in order to de-noise and eliminate any silence that may have been there (HWFDWT). Cat Swarm Optimization is used to extract features, and Mel Frequency Cepstrum Coefficients are taken into account (CSOMFCC). Classification using Modified Optimized Back Propagation Network Disorder voice Classification is then used to sort the features in the end (MOBPNDC). In terms of Accuracy, Precision, Recall, F-Measure, and Time period, the classification scheme beats the current Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) approaches. The neural speech system is a gadget that enables individuals who are unable to talk to communicate their thoughts and emotions with the outside world. It is a piece of equipment that can record the electric pulses that are generated by the brain and turn them into a synthetic voice. - Provide an overview of the concept or solution that you want to build. The electrical activity of the brain will be recorded and then sent into a synthesiser. The Synthesizer will convert the signal into voice when it has finished decoding it. The voice that has been deciphered is then supplied to an artificial voice box. The brain's electrical activity is used to generate an artificial voice, which is then output via the box.

关键词： Support vector machines Backpropagation Wiener filters Synthesizers Transforms Speech recognition Feature extraction

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Retraction Note: Efficient method for identifying prominent supplier in large-scale healthcare information networks in Medicare

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Soft Computing 2024年第1期28卷 305-305页

作者： Venunath, M. Sujatha, Pothula Dharavath, Srinu Natarajasivan, D. Koti, Prasad School of Engineering and Technology Pondicherry University Kalapet Puducherry India Department of Computer Science and Engineering Annamalai University Annamalai Nagar India Department of Computer Applications Sarada Gangadaran College Ariyankuppam India

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A Geometric Understanding of Deep Learning

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Engineering 2020年第3期6卷 361-374页

作者： Na Lei Dongsheng An Yang Guo Kehua Su Shixia Liu Zhongxuan Luo Shing-Tung Yau Xianfeng Gu DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian University of TechnologyDalian 116620China Department of Computer Science Stony Brook UniversityStony BrookNY 11794-2424USA School of Computer Science Wuhan UniversityWuhan 430072China School of Software Tsinghua UniversityBeijing 100084China Center of Mathematical Sciences and Applications Harvard UniversityCambridgeMA 02138USA

This work introduces an optimal transportation(OT)view of generative adversarial networks(GANs).Natural datasets have intrinsic patterns,which can be summarized as the manifold distribution principle:the distribution of a class of data is close to a low-dimensional *** mainly accomplish two tasks:manifold learning and probability distribution *** latter can be carried out using the classical OT *** the OT perspective,the generator computes the OT map,while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution;both can be reduced to a convex geometric optimization ***,OT theory discovers the intrinsic collaborative-instead of competitive-relation between the generator and the discriminator,and the fundamental reason for mode *** also propose a novel generative model,which uses an autoencoder(AE)for manifold learning and OT map for probability distribution *** AE–OT model improves the theoretical rigor and transparency,as well as the computational stability and efficiency;in particular,it eliminates the mode *** experimental results validate our hypothesis,and demonstrate the advantages of our proposed model.

关键词： Generative Adversarial Deep learning Optimal transportation Mode collapse

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AI for Disease Avoidance: Impact on Healthcare Personnel

AI for Disease Avoidance: Impact on Healthcare Personnel

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ammar Hameed Shnain Chandni Manral Ashish Sharma Himani S.A. Shoyusupov Vinay Kandpal Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq College of Nursing Graphic Era Hill University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Docent Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department of Management Studies Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Artificial intelligence is sometimes counted among the most powerful and practically effective scientific tools that humanity has available to it. Where AI and similar discoveries are just starting to show up in health care - However, theyre an increasing area of interest within enterprise plus society. And this progress can revolutionize various elements of healthcare - from payer, provider to even the pharmaceutical company regulations. Therefore, the goal of this study is to explore telehealth uses in infectious disease and broader health care. Methods Of The Literature On This Topic Were Searched Using Databases Like PubMed But Additionally, Google Scholar, Amazon Instructor,, Scopus, Thomson As Well As the Diary of Research. Methods The included papers for this review were selected according the complete available information. For just or whether the programs are sufficiently scaled to be helpful, keeping adherence of AI in routine clinical practice is significantly harder trouble confronting these healthcare enterprises. The synthesized data indicates that AI might make medical staff more smarter, allowing them to spend their time taking care of patients longer rather than becoming weary. Putting all this together, it could well be that "conventional medicine" is closer to the future than we might assume, where patients will see a computer before being allowed access to an actual doctor.

关键词： Infectious diseases Reviews Precision medicine Telemedicine Medical services Machine learning Telecommunication computing Personnel Artificial intelligence Medical diagnostic imaging

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Software process management in heart disease prediction using KDD

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AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Arifa Jasmin Nizar K. Anuradha Research Scholar Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Applications MES College Marampally Kerala India Professor Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

Heart disease is one of the main causes of death in India. The factors that cause heart disease are lifestyle, food habits, stress, smoking, obesity, diabetic problem etc. Healthcare Data mining has the potential to explore the hidden pattern in the data set of medical diagnostic and prognostic problems. These hidden patterns can be used for disease prediction. This research paper intends to analyze data mining techniques used in the healthcare field, particularly in heart disease prediction using Software process management (SPM). The heart disease prediction can be carried out with the help of Artificial Neural Network (ANN) and agile methodology. The parameters that investigate the use of Knowledge Discovery and Data Mining in heart disease prediction are Sex, Bad cholesterol, Blood pressure, Blood sugar, Heart rate, Age etc. This paper is an effort to design an enhanced predictive model of Software process management for heart disease prediction using Knowledge Discovery and Data Mining techniques.

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Pedometric mapping for soil fertility management – A case study

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Journal of the Saudi Society of Agricultural sciences 2021年第2期20卷 128-135页

作者： Leena, H.U. Premasudha, B.G. Panneerselvam, S. Basavaraja, P.K. Department of Master of Computer Applications Siddaganga Institute of Technology and Affiliated to Visvesvaraya Technological University Belagavi B.H. Road Tumakuru 572103 Karnataka India Department of Master of Business Administration Siddaganga Institute of Technology B.H. Road Tumakuru 572103 Karnataka India AICRP on STCR Department of Soil Science & Agricultural Chemistry UAS GKVK Bengaluru 560065 India

Information about soil physico-chemical parameters plays an important role in precision farming. To examine the relationship among soil properties, pedometric mapping is essential and has been widely applied in agricultural activities. This paper is aimed at soil nutrient assessment by generating the geospatial distribution maps for Kestur, a village in Tumakuru district of Karnataka State, India. Using random sampling technique, a total of one hundred and sixty geo-coordinated surface (0–20 cm) soil samples were collected from different land cover regions including irrigated and dryland areas in Kestur village. The samples collected were analysed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P as P₂O₅), and potassium (K as K₂O). Also, a structured questionnaire was used to collect data about demographic profile, farming practices, crops grown and fertilizers applied from each farmer under the study. The spatial distribution maps were generated for each soil property and were mapped using spatial variability by adopting Ordinary Kriging geostatistical method. Semivariogram models such as exponential for pH, spherical model for EC and OC and Gaussian for N, P₂O₅, and K₂O were used based on the best-fit selection using least Mean Error (ME) and Root Mean Square Error (RMSE) values. The outcome of the study proved that the geostatistical methods can be utilized to develop the spatial distribution maps thereby resulting in a cost effective solution to soil fertility management. © 2020 The Authors

关键词： Geostatistics Pedometric mapping Soil fertility Soil nutrient assessment

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Photonic Frequency Space Simulation of Tight-Binding Lattices Using Integrated LNOI Ring Resonators

Photonic Frequency Space Simulation of Tight-Binding Lattice...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Armandas Balčytis Hiep Dinh Tomoki Ozawa Yasutomo Ota Toshihiko Baba Satoshi Iwamoto Arnan Mitchell Thach G. Nguyen Integrated Photonics and Applications Centre RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Electrical and Computer Engineering Yokohama National University Hodogaya-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Frequency modulation Lithium niobate Resonant frequency Lattices Electro-optic modulators Ring lasers Laser modes

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Hybrid clustering algorithm for an efficient brain tumor segmentation

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Materials Today: Proceedings 2021年 37卷 3002-3006页

作者： K. Maheswari A. Balamurugan P. Malathi S. Ramkumar Department of Computer Applications School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626 126 India Department of Physics Government Arts and Science College Avinashi Tirupur India Department of Computer Science & Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

This work describes the data mining methods, techniques and algorithms used for implementation. It is an emerging field of IT industry and research. There are many other fields such as Artificial Intelligence, Machine Learning, Deep Learning, Virtualization, Visualization, Parallel Computing and Image Processing. The human internal Brain can be seen or visualized by the Magnetic Resonance Imaging scan or computerized Tomography scan. The MRI image is scanned and will be taken as input for processing. The MRI scan is more advantageous and more comfortable than CT scan for diagnosis. MRI scan provides detailed picture of organs. It does not affect the human health and body condition. It doesn't use any radiation. It is purely based on the magnetic field and radio waves. LIPC technique makes the training samples from the patients and arranges them into different group of classes used to construct different dictionaries. Image segmentation is a technique of dividing an image into different multiple portions, which is used to spot out objects and boundaries in images. There are many image segmentation techniques applicable for image processing. No acceptable method is available for solving all kinds of segmentation problem. Every method has merits and demerits. So, choosing good method is the challenging task. The hybrid clustering method is proposed in this work. The k-means algorithm and fuzzy c-means algorithm is proposed for brain tumor segmentation. The algorithm is implemented in synthetic and real time dataset. From the experimental results, this method provides better results in the form of accuracy.

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