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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Charaan, R. M. Dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, R.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities 3

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Sujatha, G. Ayyannan, Manimuthu Priya, S. Gayathri Arun, V. Arularasan, A.N. Kumar, M Jogendra Sri Venkateswara College of Engineering Department of Electronics and Communication Andhra Pradesh Tirupati India AI/ML and Data Expert San FranciscoCA United States R. M. D. Engineering College Department of Electronics and Communication Tamil Nadu Kavaraipettai India Madanapalle Institute of Technology Department of Computer Science &engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Artificial Intelligence &data Science Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350336238

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection. © 2023 IEEE.

关键词： Websites

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An Efficient Model-Agnostic Approach for Uncertainty Estimation in data-Restricted Pedometric Applications 23

An Efficient Model-Agnostic Approach for Uncertainty Estimat...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Barkov, Viacheslav Schmidinger, Jonas Gebbers, Robin Atzmueller, Martin Leibniz Institute for Agricultural Engineering and Bioeconomy Department of Agromechatronics Potsdam Germany Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Osnabrück University Semantic Information Systems Group Osnabrück Germany Osnabrück Germany

ISBN: (纸本)9798350374889

This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For ad-dressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Tomorrow’s Cities: Navigating Power Challenges through Deep Learning Solutions

Empowering Tomorrow’s Cities: Navigating Power Challenges t...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K Priyadarsini S Karthik R Tamilarasi Department of Data science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

This review, titled “Empowering Tomorrow’s Cities: Navigating Power Challenges through Deep Learning Solutions,” explores the intersection of power management issues and the trans-formative potential of deep learning technologies. It addresses the growing challenges of energy consumption, grid reliability, and peak demand, emphasizing deep learning’s vital role in optimizing power usage. The article provides a detailed overview of smart cities, highlighting the importance of efficient power management for their continued development. By presenting deep learning as a groundbreaking solution, it examines its applications in real-time monitoring, predictive analytics, and demand forecasting. Through case studies, the review showcases successful deep learning implementations in tackling specific power challenges. It also explores the integration of IoT with deep learning, emphasizing their synergy in advancing data-driven decision-making. Critical aspects like data security and privacy are discussed, ensuring a thoughtful approach to managing sensitive data for optimized power distribution. Looking ahead, the article envisions future trends and innovations, stressing the need for ongoing research to fully harness deep learning in addressing power challenges in smart cities.

关键词： Deep learning Technological innovation Smart cities Reviews Navigation Power system management Real-time systems Resource management Reliability Resilience

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction 5

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Revathy, G. Maheswari, P. Uma Madhavi, S. Vijay Anand, P. Begum, R. Vajubunnisa Jasmin, H. Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Business Systems Trichy India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Cmr Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Department of Electronics and Communication Science Teynampet Chennai India

ISBN: (纸本)9798350379945

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach. © 2024 IEEE.

关键词： Lung cancer

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Hybrid Framework for Sentiment Analysis Using ConvBiLSTM and BERT 2

Hybrid Framework for Sentiment Analysis Using ConvBiLSTM and...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Bhola, Abhishek Athithan, Senthil Singh, Shashank Mittal, Sandeep Sharma, Yogesh Kumar Dhatterwal, Jagjit Singh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Thapar Institute of Engineering and Technology Department of CSE Patiala India Rd Engineering College Department of Computer Science and Engineering Ghaziabad India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Sentiment analysis is specifically a text mining technique that utilizes natural language processing to computerize the process of analyzing text that aims to determine the sentiment expressed. The fundamental purpose of sentiment analysis is to get valuable insights that lead to all-around development in specific domains. The fantastic applications of sentimental analysis include monitoring social media, management of customer support, and customer reviews research. One of the major pitfalls in sentiment analysis is word ambiguity. To overcome this drawback, a proposed hybrid framework presented in this work is capable of dealing with such ambiguity issues. The considered evaluation parameters are accuracy, F1 score and time taken. The proposed hybrid framework utilizes Convolutional Bi-directional Long short-term memory network (ConvBiLSTM) with Bidirectional Encoder representations from Transformer (BERT) tokeniser on the given dataset and outperform other methodologies with 95.10% accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A New Tree Structure for Local Diagnosis

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Journal of Information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Image-Based Soil Health Analysis Using Deep Learning for Precision Agriculture 9

Image-Based Soil Health Analysis Using Deep Learning for Pre...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Saranya, V.S. Mangali, Saikiran Srinija, K. Medabalimi, Galeiah Devi, Meena Venkata Ramana, N.R. Lakkshmanan, Ajanthaa School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603203 India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Mlrit Department: Data science Dundigal India Sagi Ramakrishnam Raju Engineering College Sagi Ramakrishnam Raju Marg Chinna Amiram Andhra Pradesh Bhimavaram India Kcg college of technology Department of electrical and electronics engineering Chennai600097 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India

ISBN: (纸本)9798350377972

Soil health is a critical component of agricultural productivity and sustainability. Traditional methods of assessing soil health, including physical sampling and laboratory testing, are labor-intensive, time-consuming, and costly. This paper proposes a novel image-based approach for soil health analysis using deep learning techniques. By utilizing aerial images captured by drones or satellites equipped with multispectral and thermal sensors, the system non-invasively assesses key soil health indicators such as moisture content, nutrient levels, organic matter, and erosion patterns. The proposed method integrates image processing techniques with machine learning models, particularly Convolutional Neural Networks (CNNs), to extract relevant soil features from the images. These CNNs are trained to predict soil health metrics, and the system's performance is evaluated through accuracy, mean squared error (MSE), and F1-score, showing significant improvements over traditional methods. This scalable solution offers a real-time tool for precision agriculture, aiding farmers and agronomists in making informed, data-driven decisions regarding soil health management. © 2024 IEEE.

关键词： Convolutional neural networks

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