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Comparative analysis of reduced feature set based ML models for IDS

AIP Conference Proceedings 2023年第1期2773卷

作者： P. A. Jishiya K. P. Swaraj Ajay James M-Tech Student Computer Science and Engineering Govt. Engineering College Thrissur India Associate Professor Computer Science and Engineering Govt. Engineering College Thrissur India Assistant Professor Computer Science and Engineering Govt. Engineering College Thrissur India

Cyber-attacks targeting organisations and individuals are getting worse day by day. With the technological improve-ments, the principles used to launch attacks are also varied. Intrusion Detection System (IDS) is a network solution to detect attacks and anomalies in the network. Leveraging Machine Learning for developing an IDS can make it more powerful. This work aims at evaluating the performance of machine learning models for Intrusion Detection System built based on CART Algorithm, ID3Decision tree algorithm, Random forest algorithm with Convolutional Neural Network. The model is built using KDD Cup99 Dataset. In this work we perform Linear Correlation and Mutual Information feature selection algorithms for ML algorithms and for Convolutional Neural Network algorithm being a deep learning technique feature selection happens automatically. Analysing various parameters including F1-score, ID3 has shown better results.

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Introducing Service Learning To Under Graduate engineering Students Through EPICS

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Procedia computer science 2020年 172卷 688-695页

作者： Santosh Madeva Naik Surendra Bandi Hema Mahajan Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India Associate Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India

This paper describes the introduction of doing engineering through the "engineering Project in Community Service (EPICS)" and the impact of a community-based learning approach on student motivation. Students get practical hands-on experience while working with non-profit organizations. Now a day’s students find it difficult to learn in the traditional way of classroom teaching chalk and board. They want to work on real-time problems with community partners by applying engineering knowledge. EPICS program helps in grooming the students’ skills in design, communication, teamwork, and develop leadership skills, facing the challenges, social awareness, multidisciplinary knowledge, solution-driven approach towards a problem and maintaining social relationships. EPICS is one of the IUCEE (Indo Universal Collaboration for engineering Education) initiative where students are exposed to study the community needs and frame an engineering problem to work on further.

关键词： EPICS Community partner Design process Team work Products

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Artificial super intelligence: Barriers and limitations

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AIP Conference Proceedings 2024年第1期3209卷

作者： Rahul Goyal Neha Sharma Amanpreet Kaur Student Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India. Assistant Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India.

In urban studies, artificial intelligence systems are a widely recognized technology that provides an alternate approach to dealing with complicated and dynamic issues. Despite the risks of implementing more powerful artificial intelligence, many big tech companies are working towards it. So, this paper aims to show the barriers to artificial intelligence. Also, this paper illustrates barriers in machines that prevent artificial superintelligence from being achieved. Further, the paper discusses how machine neural networks differ from human neural networks and the existential threats posed by artificial intelligence and superintelligence.

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A Novel Long Short-Term Memory (LSTM)-Based Degradation Detection in Photovoltaic Systems using Performance Ratio

A Novel Long Short-Term Memory (LSTM)-Based Degradation Dete...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Amirhossein Dadashpour Aref Eskandari Amir Nedaei Sherko Salehpour Parviz Parvin Andreas Livera George Makrides Mohammadreza Aghaei Department of Electrical Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology (IUST) Tehran Iran Department of Physics and Energy Engineering Amirkabir University of Technology (AUT) Tehran Iran PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus (UCY) PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Ocean Operations and Civil Engineering Norwegian University of Science and Technology (NTNU) Alesund Norway Professor Department of Sustainable Systems Engineering (INATECH) University of Freiburg Freiburg Germany

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic (PV) systems deteriorate over time as a result of prolonged exposure to UV light and harsh weather conditions. This degradation can have a major impact on the performance of the PV systems and increase their levelized cost of energy (LCOE). To deal with this problem, in this paper, a method has been proposed based on predicting The PV systems' performance ratio, which is a measure of the actual output compared to the expected output. To estimate the performance ratio, a dataset including a PV array of normal and degraded states under different environmental conditions has been created. The main model uses an architecture with long short-term memory (LSTM). The model results show high accuracy in predicting the performance ratio of systems. The model achieved the outstanding $\mathbf{R}^{2}$ scores of $\mathbf{9 9. 4 7 \%}$ and $\mathbf{9 9. 4 6 \%}$ and the root mean squared error (RMSE) values of $\mathbf{0. 0 1 6 3}$ and 0.0169 for the training and testing processes, respectively.

关键词： Degradation Photovoltaic systems Training Accuracy System performance Predictive models Long short term memory Testing Meteorology

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IoT for Smart City Planning and Development

IoT for Smart City Planning and Development

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Raman Tirpude A. Vinitha Ahmed Kateb Jumaah Al-Nussairi Hawraa Ali Sabah Maki Mahdi Abdulhasan Manar Kareem Lafta Department of Management Research Scholar Ramdeobaba University and Assistant Professor at Maharashtra National Law University Nagpur Department of Computer Science Karpagam Academy of Higher Education Coimbatore Al-Manara College for Medical Sciences Amarah Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Technology Al-Nisour University College Baghdad Iraq Department of Pharmacy Mazaya University College Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Urban planning and development have undergone a complete transformation with the advent of the Internet of Things (IoT), and groundbreaking technologies have been made available for smart city efforts. The inclusion of Internet of Things (IoT) technology in the design and development of smart cities is examined in this article. The significance that these technologies play in improving resource management, modernizing urban infrastructure, and elevating people's quality of life is given special consideration. This paper evaluates existing Internet of Things (IoT) applications, like intelligent transportation systems, smart grids, and environmental monitoring, to show how the Internet of Things (IoT) might address common urban issues like traffic congestion, energy consumption, and environmental pollution. Furthermore, the essay examines case studies submitted by well-known smart cities to show how the Internet of Things (IoT) may be successfully used and how it affects the sustainability and efficiency of metropolitan areas. The results suggest that the Internet of Things not only facilitates real-time data collection and analysis but also fosters the creation of creative urban development ideas. Thanks to the research that was done, this study shows how the Internet of Things has the potential to impact future cities and make them more resilient, adaptable, and effective in meeting the needs of growing populations.

关键词： Pollution Smart cities Urban planning Transportation Data collection Real-time systems Smart grids Internet of Things Environmental monitoring Sustainable development

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Site reliability engineering for IOS mobile application in small-medium scale industries

Global Transitions Proceedings

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Global Transitions Proceedings 2021年第2期2卷 137-144页

作者： N Kavyashree Dr. Supriya M C Dr. Lokesh M R SSAHE Tumkur Assistant Professor Dr.AIT Bangalore Karnataka India Department of MCA SSIT Tumkur Karnataka India Department of computer Science and Engineering MITM Mandya Karnataka India

Software reliability is one and only the standard used to calculate the excellence of a software system. In this situation, we suggest a method to forecast software reliability in the primary creation of any process growth phases since from the designing stage only. Software mobile application has remained a significant tool in our lifetime, and several small-medium scale software progress companies capitalize the substantial resources to mark accessible prospects. The landscape of mobile atmospheres varies as of server environments owing to various issues, by means of the net, liveliness, battery-operated, and compatibility. Estimating and forecasting mobile application reliability remain the actual contests as of the variety of the mobile situations in which the applications are used, and the absence of widely available fault data. In addition, error information is optionally given in to the end-users. Hence, in what way to estimate reliability of mobile software application has remained more and more significant. In this paper, site reliability engineering (SRE) for mobile application is proposed for the reliability of software and is taken as a case study. Their failure is copied back to the exact features of mobile application is useful and powerful.

关键词： Fault report Mobile application Site reliability engineering Software forecast Software reliability SRGM Model

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Reinforcement learning: A novel approach towards drug discovery

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AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

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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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Preservation of data using magic squares in Asymmetric key cryptography

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Materials Today: Proceedings 2022年

作者： Srinivasa Rao, Mannava Murari, Thejovathi Sukirtha Priya, N.D. Raghunandan, K.R. Dept of ECE PVP Siddhartha Institute of Technology India Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur India Dept of CSE Government College of Engineering Salem India Senior Assistant Professor Dept of ECE N.M.A.M. Institute of Technology Nitte affiliated to VTU Belagavi India

In data communication the information that is being communicated should be confidential. Many algorithms have been designed to keep the data secure. But malicious attacks on these algorithms to break its security have made it necessary to design new algorithms. To overcome this problem a new method using magic squares has been proposed in this paper. The magic square is constructed on constraints such as order of the matrix n and starting number a. © 2021

关键词： Cryptography

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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

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computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection Systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

关键词： 2D-LICM hyper-chaotic map with Walrus Optimization (2D-LICMHy-CM_WO) Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) Double Deep Q-Learning Network (DoubleDQN) Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE)

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