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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Indhuja, E. Angelina, J.Jane Rubel Subhashini, S.J. Ajay Kumar, B. Amulya, L. Gopi, G. Kalasalingam Academy of Research & Education Department of Computer Science and Engineering Krishnankovil India Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Sivagangai India

ISBN: (纸本)9798350300093

This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities. © 2023 IEEE.

关键词： Information management

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

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Enhancing Crop Yield and Growth Prediction Using IoT-Based Smart Irrigation Systems and Machine Learning Algorithms 2

Enhancing Crop Yield and Growth Prediction Using IoT-Based S...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shilpa, M. Ravi, P. Sharmila, N. Mallikarjunaswamy, S. Deepak, B.L. Pavithra, G.S. Thazeen, Sadiya Komala, M. Mahendra, H.N. Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Mysore College of Engineering & Management Dept of Computer Science and Engineering Mysuru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjc Institute Of Technology Dept of Information Science and Engineering chickballapur India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper introduces 'Smart Irrigation Yield Optimization (SIYO),' a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation methods such as furrow irrigation, overhead sprinkler systems, and manual watering are analyzed for their inherent inefficiencies, especially in precision and resource management. SIYO overcomes these drawbacks by employing a network of real-time sensors that monitor soil moisture, temperature, humidity, and nutrient levels, thus enabling precise and automated irrigation tailored to the specific needs of crops. This technology not only optimizes water and nutrient usage but also adapts to varying environmental conditions to maximize crop potential. Comparative studies reveal that SIYO enhances water efficiency by 0.30% over furrow irrigation, increases nutrient utilization by 0.25% compared to overhead systems, and boosts the accuracy of crop yield and growth predictions by 0.40% against manual methods. The implementation of SIYO demonstrates the substantial improvements in agricultural productivity and resource sustainability achievable with advanced IoT technologies. © 2024 IEEE.

关键词： Smart agriculture

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Poster Abstract: On the Accuracy and Robustness of Large Language Models in Chinese Industrial Scenarios 23

Poster Abstract: On the Accuracy and Robustness of Large Lan...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Li, Zongjie Qiu, Wenying Ma, Pingchuan Li, Yichen Li, You He, Sijia Jiang, Baozheng Wang, Shuai Gu, Weixi Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong China Academy of Industrial Internet Beijing China

ISBN: (纸本)9798350362015

Recent studies have demonstrated that large language models (LLMs) exhibit exceptional performance across various natural language processing tasks, rivaling or even exceeding human competencies in certain areas [1] - [5]. Typically, LLMs undergo pre-training on extensive text corpora, usually using billions of tokens to develop a foundational model. To better align LLMs with human preferences and directives or to fulfill specific application needs, methods such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO) have been introduced and demonstrated to be effective. These advancements facilitate more intuitive and efficient human-AI interactions. However, the substantial resource requirements throughout the training process pose challenges for individual users and smaller organizations. © 2024 IEEE.

关键词： Reinforcement learning

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Ground-Glass Lung Nodules Recognition Based on CatBoost Feature Selection and Stacking Ensemble Learning

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Journal of Shanghai Jiaotong University (science) 2024年 1-10页

作者： Miao, Jun Chang, Yiru Chen, Chen Zhang, Maoxuan Liu, Yan Qi, Honggang Guo, Zhijun Xu, Qian School of Computer Science Beijing Information Science and Technology University Beijing100101 China Pathological Information Engineering Technology Center Jinan Supercomputing Technology Research Institute Jinan250100 China School of Computer Science and Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Radiology Tianjin Kanghui Hospital Tianjin300385 China Department of Radiology Huabei Petroleum General Hospital Hebei Renqiu062550 China

Aimed at the issue of high feature dimensionality, excessive data redundancy, and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition, a recognition method based on CatBoost feature selection and Stacking ensemble learning was proposed. First, the method uses a feature selection algorithm to filter important features and remove features with less impact, achieving the effect of data dimensionality reduction. Second, random forests classifier, decision trees, K-nearest neighbor classifier, and light gradient boosting machine were used as base classifiers, and support vector machine was used as meta classifier to fuse and construct the ensemble learning model. This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers. The experimental results show that the recognition accuracy of the proposed method reaches 94.375%. Compared to the random forest algorithm with the best performance among single classifiers, the accuracy of the proposed method is increased by 1.875%. Compared to the recent deep learning methods (ResNet+GBM+Attention and MVCSNet) on ground-glass pulmonary nodule recognition, the proposed method’s performance is also better or comparative. Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules. © Shanghai Jiao Tong University 2024.

关键词： Feature Selection

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Astrometric Observations of NEA 1998 HH49 Using the Daocheng 50 cm Telescope

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Research in Astronomy and Astrophysics 2024年第7期24卷 71-78页

作者： Huan Xu Xiang-Ming Cheng Yi-Gong Zhang Teng-Fei Song Zhen-Jun Zhang Qing-Yu Peng Yunnan Observatories Chinese Academy of SciencesKunming 650216China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of SciencesKunming 650216China Engineering Laboratory on Applied Astronomical Technology of Yunnan Kunming 650216China School of Information Engineering Kunming UniversityKunming 650214China School of Computer Science and Information Engineering Anyang Institute of TechnologyAnyang 455000China Department of Computer Science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China

This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical *** the 50 cm telescope located at the Wumingshan Mountain in Daocheng,Sichuan,images were obtained over four nights,from 2023 October 19 to October *** observations were processed using Astrometrica software,facilitating the precise determination of the asteroid's *** observational results were compared with the ephemerides from three distinct sources to verify accuracy:the Jet Propulsion Laboratory(JPL)Horizons System,the Institut de Mécanique Céleste et de Calcul deséphémérides(IMCCE)Miriade,and the Near-Earth Objects Dynamic Site(NEODyS-2).When compared with the JPL ephemeris,a mean observed-minus-calculated(O-C)result of 0.″07 in the *** and-0.″35 in the *** was ***,the comparison with the IMCCE ephemeris yielded mean O-C results of 0.″08 in the *** and-0.″06 in the *** comparison with the NEODyS-2 ephemeris yielded the mean O-C results of 0.″06 in ***-0.″49 in *** study's findings demonstrate a general consistency between the observed data and the ephemeris predictions,with minor discrepancies observed across the data ***,both the JPL and NEODyS-2 ephemerides show that the residuals in the *** exceed those in the *** disparities may result from atmospheric differential color refraction,ephemeris discrepancies,observational errors,and other ***,it is worth noting that further investigation is required due to the potential influence of additional ***,the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.

关键词： astrometry ephemerides time

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Performance Analysis of Machine Learning Algorithms to Predict Water Potability 1

Performance Analysis of Machine Learning Algorithms to Predi...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Parnika, J. Devamane, Shridhar B Ramya, C.N. Swarna Laxmi, M.G. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350339727

The survival of all living organisms, industry and agriculture is dependent on freshwater availability. A crucial step in the management of freshwater is the examination of the water's quality. According to the annual report of the World Health Organization, a lack of safe drinking water is resulting in the illness or death of many people, particularly children and pregnant women. Before the water is used for any purpose, including chemical spraying (for pesticides, etc.), or drinking water, it is essential to test its quality. Finding water that is safe to consume can be done to some extent by testing the water's quality. Therefore, water quality testing is essential. In this paper, we have carried out the implementation of 3 machine learning algorithms to check for potability of water after balancing, selecting important features using the Univariate Selection method and standardizing the data. Random Forest Classifier gave the maximum accuracy of 88.75%. The main objective of our work is to increase the accuracy and demonstrate the performance analysis of the considered algorithms' accuracy in this paper along with the existing works. © 2023 IEEE.

关键词： Decision trees

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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Prediction and Comparative Analysis of the Stress Level of Humans by Using the Machine Learning Algorithms 4

Prediction and Comparative Analysis of the Stress Level of H...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sivaraj, C. Angeljulie, J. Hemamalini, D. Neerugatti, Vikram Panchal, Narayan Mahesh, T.R. Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamilnadu Coimbatore India Arjun College of Technology Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350387490

Some of the techniques that have been used include Naive Bayes, K-Means Clustering, Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). To provide useful information to researchers and practitioners, the study reflects the performance, strengths and weaknesses of each algorithm using several datasets. The ability of SVM to model complex decision boundaries is evaluated. It is widely applied in high-dimensional scenario. This paper focuses on the analysis of the advantages and disadvantages of using KNN for classification purposes, such as its simplicity of use, flexibility, and the fact that the classification accuracy and the sensitivity to the selection of parameters should not be overlooked. The performance of the unsupervised K-Means Clustering algorithm in partitioning data into clusters is assessed. The method of decision trees is considered, and the question of their capability to model non-linear dependencies is raised. Finally, the performance and relevancy of the probabilistic classifier Naive Bayes for text categorization is also evaluated. This research thus offers valuable guidance to practitioners regarding the strengths and weaknesses of the proposed algorithms for categorisation, based on a detailed comparative analysis. © 2024 IEEE.

关键词： k-nearest neighbors

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