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international Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Vibhor sharma Pramod Kumar suman Pant Deepak srivastava Yogesh Kumar sharma Department of Computer Science & Engineering Himalayan School of Science & Technology Swami Rama Himalayan University Dehradun India Department of Computer Science & Engineering I.T.S Engineering College Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Clustering is a way to discover groupings in a dataset without using supervised learning. it is an NP-hard problem to cluster data. in most cases, machine learning approaches are necessary to solve the information clustering issue. Affinity dissemination is one of the more recent ideas for cluster detection. the example principle underpins the affinity transmission framework. similarities between data points are used as input in the affinity dissemination method. Using this approach, you can get a collection of data points that are good representations of the clusters. in order to create clusters, all non-exemplar information points are assigned to neighbouring exemplars, which are known as exemplars. in this paper, we proposed K-means based on Diverse transfer Learning (DtL). the beginning of centres determines the clusters in the K-Means result. this problem is attempted to be solved using K-Means based on DtL. the DtL-based K-means algorithm begins by using K-Means to locate the text cluster centre. the following stage involves using these text centroids to establish the clustering using K-mean centres for images. Despite being just as sophisticated as K-Means, DtL-based K-Means produces better outcomes. We conducted experiments with the K-Means clustering approach and K-Means clustering approach based on Diverse transfer Learning and determined their F-Measure, Clarity, and Entropy scores for comparison.

关键词： NP-hard problem transfer learning supervised learning Clustering algorithms Grid computing Entropy

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transaction Cost Analysis for a High security Role-Based Access Control Vending Machine Using Blockchain

Transaction Cost Analysis for a High Security Role-Based Acc...

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Advancement in Electronics & Communication engineering (AECE), international Conference on

作者： Yogesh Kumar sharma Vipin Kumar Kushwaha Akansha sharma Murari Kumar singh Computer Science and Engineering I.T.S Engineering College Greater Noida U.P. Computer Science and Engineering IEC College of Engineering and Technology Greater Noida U.P. Computer Science and Engineering Sharda University Greater Noida U.P

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

this paper will present a blockchain-based, high-security, role-based access control (RBAC) vending machine system. the vending machine shall implement stringent role-based permissions wherein only users with the designated `MANAGER_ROLE` can add, update, or restock items. Only the contract owner, led exclusively to his address, has this role and thus is authorized to assure controlled management. items will be mapped with unique iDs for scalability and organization. Every item shall have its corresponding assigned `purchase limit`, thus limiting the number of units an individual user may buy to prevent customers' exploitation. the smart contract enforces these limits by halting in excess Ether sent accompanying a transaction. it will, however allow the users to buy items by only sending Ether to the contract, so that all purchases will observe set limits. the combination of blockchain and RBAC would provide much-needed security and transparency to vending machine operations, making this solution very robust for decentralized commerce. it will, however, let users easily purchase items just by sending Ether to the contract, ensuring that all purchases observe set limits. this combination of blockchain and RBAC would provide much-required security and transparency to vending machine operations, making this solution very robust for decentralized commerce

关键词： Access control Limiting scalability smart contracts Organizations Blockchains Cost benefit analysis

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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sN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, saranya Narendran, Muthusamy sathar, Banu sengan, sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (Ai) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. Ai algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. this study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. the study makes KDP more accurate and reliable using RL methods on clinical data. this lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of Ai systems over time, enhancing their effectiveness in detecting and diagnosing diseases. in the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). the recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Hourly trend Analysis of spatio-temporal Precipitation Extremes Using tRMM and GPM satellite Data on EtCCDi indices for the indian Region 3

Hourly Trend Analysis of Spatio-temporal Precipitation Extre...

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3rd international Conference on Communication, Control, and intelligent systems, CCis 2024

作者： Hingrajiya, Heli Bodana, Yuvrajsinh Amin, Khyati thakkar, Amit shashtri, Hiteshri C.S.P.I.T Charusat Department of Computer Science and Engineering India Tix Ecosystems Pvt.Ltd Vadodara India

ISBN: (纸本)9798331528201

india relies heavily on rainfall for agriculture, water management, and climate planning. satellite missions like the tropical Rainfall Measuring Mission (tRMM) and Global Precipitation Measurement (GPM) provide global rainfall data, but they haven't been fully used for studying daily or hourly rainfall in india. this research looks at how daily data from these satellites can help analyze rainfall patterns over time. it uses methods like the Mann-Kendall test and sen's slope estimator, compatible with handling satellite data. the study analyzes each hour starting from the 00th to the 21st (00th, 03rd, 06th, 09th, 12th, 15th, 18th, 21st), i.e., daily data split into 3-hour intervals to infer insights into diurnal changes within precipitation patterns. Moreover, through this process, the recent shift of extreme rainfall activities from the Bay of Bengal towards the Arabian sea is also addressed, and a call for action is made through which the government and meteorological organizations can draw meaningful conclusions for early future calamity prediction. © 2024 iEEE.

关键词： Rain

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A Multimodal Content Moderation system using Adversarial Machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

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3rd international Conference on Applied Artificial intelligence and Computing, iCAAiC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, s. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. it is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LstM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLstM and CNN have been used to extract contextual features. this study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. the proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. the proposed system uses both text and image data. to improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 iEEE.

关键词： image enhancement

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implementation and Performance Evaluation of Asymmetrical Encryption scheme for Lossless Compressed Grayscale images 4th

Implementation and Performance Evaluation of Asymmetrical En...

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4th EAi international Conference on Cognitive Computing and Cyber Physical systems, iC4s 2023

作者： Gupta, Neetu Gupta, Hemant Kumar swapna, K. Raju, Kommisetti Murthy srivastava, Rahul Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Electronics and Communication Engineering Arya College of Engineering & I.T. Rajasthan Jaipur India Department of Electronics and Communication Engineering Vaagdevi College of Engineering Telangana Warangal India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimawaram India

ISBN: (纸本)9783031488870

the employment of compression and encryption methods enables the transmission of images across a communication link with less bandwidth consumption and resistance against differential assaults. in this study, the gray scale images are compressed using the Huffman lossless compression approach. Each pixel is given a unique prefix code with a configurable length in this. the frequency of occurrence of characters has an inverse relationship with prefix code length. Asymmetrical RsA encryption is used to encrypt compressed images. in the RsA encryption technique, the encryption key is kept in the open as opposed to the decryption key, which is kept private. Analysis of the compression parameters, together with correlation coefficient and entropy analysis, is used to convey the effectiveness of suggested techniques. Five 512 × 512 grayscale test images are used as text images and to verify the results. © 2024, iCst institute for computer sciences, social informatics and telecommunications engineering.

关键词： Cryptography

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A novel framework for highly contagious diseases deaths prediction using machine learning techniques

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international Journal of information technology (singapore) 2024年第5期16卷 2795-2802页

作者： Hasan, sani siddiqui, tamanna Mustaqeem, Mohd Khan, Najeeb Ahmad A.K.T.U. I.T.M College Aligarh India Department of Computer Science Aligarh Muslim University (AMU) Aligarh India Faculty of Engineering and Technology Arunachal University of Studies Namsai India

the recent pandemic has shown us how a new pathogen can infect previously infected individuals again and the current data systems do not account for that in the siR Model. During the pandemic, one of the more significant concerns in developing nations was the survival and livelihoods of the people below the poverty line. As these people lacked the necessary means to endure the long and hard-hitting impact of the pandemic on the economy, it led to an inability to follow the official guidelines due to the eminent need for survival. However, this also had another repercussion: the increase in the spread of the disease caused spikes in the number of cases over time. this has led us to believe that by using the percentage of a country's population living under the poverty line, we may be able to hypothetically calculate the number of deaths in a future pandemic under similar economic and social conditions. © 2023, the Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： COViD-19—severe acute respiratory syndrome coronavirus 2 (sARs-CoV-2) iFR—infection fatality rate siR—susceptible-infectious removed

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two-stage Deep Learning - Youtube Video Recommendation Process

Two-Stage Deep Learning - YouTube Video Recommendation Proce...

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2023 international Conference on New Frontiers in Communication, Automation, Management and security, iCCAMs 2023

作者： Balakrishna, Cheepu sharada Mani, D. Basi Reddy, A. Chandrakanth, P. selvan, R.senthamil Saispurthi Institute of Technology Department of Computer Science and Engineering Sathupally Mandal India Qis College of Engineering and Technology Department of Computer Science and Engineering Ongole India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India N.B.K.R.I.S.T Vidyanagar Department of Computer Science and Engineering Kota India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350317060

Youtube is one of the most widely used and technically advanced commercial systems of recommendations. it provides an overview of the system and highlights the remarkable gains in efficiency made possible by deep learning. this research uses the traditional 'two-stage' framework for separating and retrieving information. First, a profound model for generating candidates ought to be described, and then a model for ranking candidates ought to be outlined, and both of these should be done in that order. the order in which these two tasks ought to be completed is important. in addition, it offers instruction that can be put into practice as well as valuable insights that have been gleaned from the process of developing, iterating, and managing a large-scale recommendation system that has a significant impact on the experience of the user. © 2023 iEEE.

关键词： Deep learning

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An Efficient Framework to Diagnose Diseases using Medical image segmentation and Level set Method 14

An Efficient Framework to Diagnose Diseases using Medical Im...

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14th international Conference on Advances in Computing, Control, and telecommunication technologies, ACt 2023

作者： Pancholi, Priti Vijaykumar Anandarao, Dhaigude tanaji Peddi, Prasadu Department of Computer Science and Engineering S.J.J.T.U University Vidyanagari Rajastan Jhunjhunu333001 India Department of Computer Science and Engineering Fabtech Technical Campus College of Engineering and Research Maharastra. Sangola India

the rise in accessibility and use of contemporary medical imaging has resulted in a significant growth in the need for automatic medical picture segmentation. these methodologies have demonstrated significant potential for both the development of computer simulations and the analysis of considerably more complicated geometries in applications both medical and non-medical in nature. An application of the level set approach in conjunction with the fuzzy methodology is presented in this research for the purpose of picture segmentation. We propose the two-stage technique known as Mist (Medical image segmentation tool) as a means of achieving our goal of finding the approach that is most suited for the segmentation of medical images. © Grenze scientific society, 2023.

关键词： Diagnosis

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Assessment of deep neural network for prediction of chronic kidney disease 14

Assessment of deep neural network for prediction of chronic ...

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14th international Conference on Computing Communication and Networking technologies, iCCCNt 2023

作者： Gupta, Neetu Gupta, Hemant Kumar Vaishali, s. sreedevi, Bobbala Manipal University Department of Computer Science & Engineering Rajasthan Jaipur India Arya College of Engineering & I.T. Department of Electronics & Communication Engineering Rajasthan Jaipur India Vaagdevi College of Engineering Department of Electronics & Communication Engineering Telangana Warangal India

ISBN: (纸本)9798350335095

this study uses deep learning techniques to detect chronic kidney disease at earlier stage to aid in its prevention. Disorders that affect the kidney's natural function are referred to as kidney diseases. this research recommends using a perceptron classifier with multiple layers classifier that is based on deep neural networks to identify CKD in patients. in order to diagnose chronic kidney disease, a database of 400 individuals with 25 attributes was employed. For the tests, the means of the respective attributes were used to replace each missing value in the original data set. the deep neural network's optimal parameters were then produced by fine-tuning the parameters and running several trials. in this research, A deep neural network is designed and developed. the performance of proposed model is compared with various cutting-edge machine learning techniques. Experiments show that the proposed model performs with 99.85% testing accuracy in classification tasks. © 2023 iEEE.

关键词： Deep neural networks

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