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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Murugan, Suganiya Srividhya, S.R. Kumar, S. Pradeep Rubini, B. Srm Inst Itute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vels Inst Itute of Science Technology and Advanced Studies Department of Elect Rical and Elect Ronics Engineering Chennai India

ISBN: (纸本)9781665474672

In medical science, dermatology is the department dedicated to identify, diagnose, and suggest a treatment for skin disease. Diseases that occur on human skin is mostly unpredictable and tough to analyze, in recent days, the occurrence of skin diseases increased unprecedently. Skin diseases are usually caused by bacteria or other related infections. Some skin diseases like ringworm, yeast infection, allergies etc. will constantly increase and spread over the skin. This type of disease should be identified in its earlier stage to avoid spreading. The identification can be done by considering some factors like clinical parameters, which are considered for identifying the disease. The possible skin diseases are dermatitis for the age group 0-5 years, warts affect 6-11 years old children, and acne vulgaris affect 12-16 years. Dermatomyositis is a type of skin disease that affects children aged 5-15 and adults aged 40-60. Due to unawareness, patients usually ignore the early symptoms. This necessitates the need for skin disease prediction. This research work predicts the type of disease by using the clinical parameter of a patient, which is achieved by using various machine learning classification algorithms. Maximum accuracy of 98.4% is achieved by the Naive Bayes algorithm. The Graphical User Interface (GUI) was developed for easy utilization of this prediction module for both doctors and dermatologists. © 2023 IEEE.

关键词： Forecasting

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Innovative Solutions for Sustainable IoT in Smart City Infrastructure

Innovative Solutions for Sustainable IoT in Smart City Infra...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Arul P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants’ safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application.

关键词： Smart cities Reviews Video surveillance Cameras Robustness Real-time systems Safety Blockchains Recording Research and development

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Secure Data Embedding in Digital Images: Enhancing Covert Communication with LSB-Based Techniques

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Procedia computer science 2025年 258卷 2091-2100页

作者： Tina Babu Rekha R Nair Rajesh T M Kishore S Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore Karnataka India

Information security and the ability to convey secret messages have been necessities since the dawn of civilization. Various media forms, such as photos, audio files, and videos, offer a conducive environment for concealing information that is invisible to the human eye in the current digital era by taking advantage of superfluous or unused data bits. This hidden information can be used for secret communication or as a way to include more messages into a single file, which will minimize the quantity of data that needs to be sent. In order to encode and store messages within the RGB pixels of a cover image (canvas image) without causing visual distortion, two methods are shown in this study. Both approaches split the message into separate segments, which are then translated into 8-bit binary values. These values are then encrypted using a straightforward symmetric exclusive OR (XOR) encryption key, and the message is encoded in the canvas image by changing the pixel value’s least significant bit (LSB). The first approach encodes the secret message from top to bottom and left to right by sequentially storing it beginning with the top-left pixel. By encoding the message bits into the cover image pixels at random, the second technique reduces the susceptibility of the concealment to analysis. By using redundant data in digital media, these approaches can hide information while preserving the original visual integrity. This makes them an effective and safe way to communicate covertly or embed data into a single file, which maximizes data transmission.

关键词： Steganography Least Significant Bit (LSB) Data Encryption Covert Communication Digital Media XOR Encryption

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification

Towards Sustainable Waste Management: Exploring Machine Lear...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： S. Sasi Rekha P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Pallavaram India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This study examines 20 research articles focused on AI-based waste management techniques, including machine learning, deep learning, and IoT sensors. Researchers aim to develop sustainable solutions for waste sorting and management. The articles highlight the significant role of deep learning and computer vision in waste classification, with a growing interest in recent years, particularly in 2022. These advanced technologies offer promising avenues for enhancing waste sorting accuracy, reducing manual labor reliance, and promoting recycling efforts. Moreover, the incorporation of IoT sensors plays a vital role in continuously monitoring waste levels and environmental parameters, thereby optimizing the entire waste collection and management workflow. These outcomes underscore the escalating adoption of sophisticated AI methodologies in waste management, suggesting a paradigm shift towards enhanced and environmentally conscious waste management practices. This research provides valuable insights for policymakers, waste management practitioners, and researchers seeking to address the challenges of waste management while promoting environmental sustainability.

关键词： Waste management Deep learning Pervasive computing Social networking (online) Green products Manuals Sensors

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A codesign framework for online data analysis and reduction

A codesign framework for online data analysis and reduction

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作者： Mehta, Kshitij Allen, Bryce Wolf, Matthew Logan, Jeremy Suchyta, Eric Singhal, Swati Choi, Jong Y. Takahashi, Keichi Huck, Kevin Yakushin, Igor Sussman, Alan Munson, Todd Foster, Ian Klasky, Scott Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Department of Computer Science University of Maryland College ParkMD United States Division of Information Science Nara Institute of Science and Technology Nara Japan Oregon Advanced Computing Institute for Science and Society University of Oregon EugeneOR United States Department of Computer Science The University of Chicago ChicagoIL United States

science applications preparing for the exascale era are increasingly exploring in situ computations comprising of simulation-analysis-reduction pipelines coupled in-memory. Efficient composition and execution of such complex pipelines for a target platform is a codesign process that evaluates the impact and tradeoffs of various application- and system-specific parameters. In this article, we describe a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for composing parametric studies on coupled applications, and Savanna, a runtime engine for orchestrating and executing campaigns of codesign experiments. This toolset facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage versus compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next-generation simulations that make use of online processing techniques. We illustrate the design of Cheetah and Savanna, and present application examples that use this framework to conduct codesign studies on small clusters as well as leadership class supercomputers. © 2021 John Wiley & Sons Ltd.

关键词： Data reduction

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Analysis of Cardiac Disease based on AI Prediction Techniques using data analytics approach

Analysis of Cardiac Disease based on AI Prediction Technique...

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Soft computing for Security Applications (ICSCSA), International Conference on

作者： R. Sivanathbabu S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Patients who have an increased likelihood of coronary heart disease can reduce their consequences by changing their lifestyle, with the support of early diagnosis. Healthcare expenses are rising above, both company budgets and the average cost of medical treatment nationally, due to asymptomatic conditions like cardiovascular disorders. Early detection and treatment of these illnesses are essential. Utilizing the data that is accessible in healthcare systems has been significantly impacted by recent technological advancements. As part of this, patients' Heart Disease (HD) may be predicted using the massive amount of data. However, due to concerns with prediction and diagnosis performance and accuracy, projecting HD has emerged as one of the main obstacles facing the healthcare sector. The main challenges were to apply homogeneous data mining techniques to define the most significant causes of heart disease and to accurately predict the overall risk. Using Data Analytics (DA) to speed up the prediction process is one way to make use of massive amounts of health care data. Selecting the optimal analytical methods, however, is the most crucial step as it will impact the final result of the diagnostic and forecasting. In order to assess the effectiveness of the methods for HD forecasting, this study intends to implement DA approach in collecting of databases. In this research work, Best First Search (BFS) has been used to identify the most important attributes for HD prediction. This study employs a range of ML approaches based on AI to predict HD. The Random Forest (RF) method, which uses specific characteristics, has the highest accuracy rate (98.63%) in this experiment. The BFS extracted 14 relevant features and then compared them using AI approaches. Based on the analysis, feature selection has enhanced the DA technique by improving the efficiency and precision of the classification model's prediction.

关键词： Heart Radio frequency Analytical models Accuracy Data analysis Medical services Predictive models Feature extraction Forecasting Diseases

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Advancements in Skin Disease Diagnosis Through Machine Learning-Driven Lesion Segmentation Techniques

Advancements in Skin Disease Diagnosis Through Machine Learn...

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International Conference on Inventive Computation Technologies (ICICT)

作者： M. Megala T.R. Nisha Dayana Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced-Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Skin diseases pose a significant global health challenge, demanding precise diagnosis and effective treatment. This survey paper delves into the realm of enhancing skin disease diagnosis via a pioneering machine learning algorithm focused on lesion segmentation. By amalgamating cutting-edge techniques like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), this algorithm strives to elevate accuracy and efficiency in this pivotal task. The results unequivocally establish the algorithm's superiority in accurately delineating skin lesions. Timely identification and meticulous characterization of skin ailments substantially influence patient outcomes. This contribution augments the corpus of knowledge in machine learning-based medical image analysis. The amalgamation of sophisticated techniques within this algorithm unfurls new horizons for subsequent exploration and innovation in skin disease analysis. The continuous evolution of this field harbors the potential to revolutionize dermatological practice, thereby enhancing the global management of skin disorders. The machine learning algorithm for skin lesion segmentation unveils a propitious avenue for accurate and efficient skin disease diagnosis. The far-reaching impact on patient care and the broader dermatological sphere underscores the imperative of further investigation and validation of this approach.

关键词： Surveys Technological innovation Machine learning algorithms Accuracy Generative adversarial networks Skin Lesions Medical diagnosis Convolutional neural networks Diseases

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Deep Generative Decision Neural Network Approach for Effective Early Prediction of Meninges

Deep Generative Decision Neural Network Approach for Effecti...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： A. Shabana P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Meningitis is a contagious disease that leads to neurocognitive impairment. It is caused by inflammation in the protective membranes surrounding the brain and spinal cord and can be caused by various pathogens, such as viruses and bacteria. It is necessary to make predictions and classify meninges diseases. The conventional techniques for forecasting meninges are time-consuming and prone to errors, resulting in low accuracy rates with high false classifications. To tackle these problems, this study presents a Deep Learning (DL) method to aid in predicting meningitis. The novel introduces a Deep Generative Decision Neural Network (DGDN2) algorithm for predicting meninges. The Box-Plot Standardization (BPS) algorithm eliminates null and missing values to prepare the dataset. Next, the Mutual Gain Genetic Algorithm (MG2A) is utilized to select essential features of meninges diseases. Finally, the DGDN2 algorithm is used to predict meninges diseases efficiently. According to experimental results, the proposed model achieves higher accuracy, precision, recall, and F-measure than previous methods.

关键词： Pathogens Accuracy Spinal cord Computational modeling Standardization Medical services Predictive models

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