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Proceedings of 2023 2nd international Symposium on computing and Artificial Intelligence, ISCAI 2023

Proceedings of 2023 2nd International Symposium on Computing...

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2nd international Symposium on computing and Artificial Intelligence, ISCAI 2023

ISBN: (纸本)9798400708954

The proceedings contain 21 papers. The topics discuss include: a hybrid improved lion swarm optimization algorithm;optimization of Chinese character font recognition model based on incremental learning;research on the application of artificial intelligence technology in material support;processing approaches for functional magnetic resonance imaging data in a limited sample of deaf individuals;navigating the confluence: advancements and trends in artificial intelligence-driven design education based on CiteSpace’s visual analytics;effects of subsidy and cap-and-trade allocation rules on carbon management of multi-party supply chain;towards feature selection for detecting LDDoS in SD-IoT of smart grids: a multi-correlation information EA-based method;and the design of an intelligent security system with face recognition and human action analysis.

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Implementation of Machine Learning Techniques for Predictive Security analytics 12th

Implementation of Machine Learning Techniques for Predictive...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Moon, Pradnya S. Deshmukh, Anand B. Sarode, Harsha Jitendra Sharma, Shweta Birare, Komal Madhukar Anandpwar, Winit Nilkanth Department of Computer Technology Yashwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Information Technology Sipna College of Engineering and Technology Maharashtra Amravati India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India Department of Artificial Intelligence and Data Science Arya College of Engineering Rajasthan Jaipur India Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science Dr. Ambedkar College Deekshabhoomi Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Machine learning (ML)-based predictive security analytics has become an important part of modern defense. This study gives a brief overview of how machine learning can be used in predictive security analytics. Traditional security measures aren’t working as well as they used to because online risks are getting smarter and more complicated. We need to use proactive and predictive strategies instead. Machine learning systems can look through huge amounts of data and find trends and outliers that could mean an attack or security breach. Using controlled, unstructured, and reinforcement learning, predictive security analytics can spot problems early on and stop them before they become a real problem. Supervised learning algorithms, like support vector machines and neural networks, let security events be put into groups based on data that has already been named. This makes it easier to find threats. Unsupervised learning methods, like grouping and anomaly detection, make it easier to spot strange behavior that could be a sign of a security breach even without any training data. Because they learn from how they interact with their surroundings, reinforcement learning models let security systems change and get better over time. By adding machine learning models to security data tools, companies can improve their cyber defenses, which lets them proactively reduce threats and control risks. Furthermore, the ability of ML algorithms to learn new things all the time makes it possible for security measures to be flexible enough to react to changing dangerous environments. However, problems like bad data, models that are hard to understand, and threats from other people mean that ML for predictive security analytics needs to be carefully thought out and implemented using strong methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cloud-based smart Sewage Management System for Efficient Wastewater Treatment 4

Cloud-based Smart Sewage Management System for Efficient Was...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Dhayalan, V. Raman, Ramakrishnan Kalaivani, N. Shrirvastava, Ankit Rajasekar, G. Thamizhamuthu, R. Sri Sairam Engineering College Tamil Nadu Chennai India Symbiosis Institute of Business Maharashtra Pune India Saveetha College of Liberal Arts & Sciences Tamil Nadu Chennai India Maharashtra Pune India Vinayaka Mission's Kirupananda Variyar Engineering College Tamil Nadu Salem India Kattankulathur Campus-Chengalpattu Srm Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

Maintaining public health and environmental sustainability depend on effective wastewater treatment. Monitoring, controlling, and optimizing the treatment process are often difficult for traditional sewage management systems. An innovative sewage management system that is cloud-based seems to be a feasible option in this situation. This system uses cloud computing and Internet of Things (IoT) technology to build an intelligent, connected network for monitoring and managing wastewater treatment operations. Sensors are installed throughout the sewage system to collect real-time data on numerous factors, including flow rate, pollutant levels, and water quality. It is sent to a central cloud platform to store and analyze this data. The cloud platform processes the collected data and produces valuable insights using innovative data analytics methods. It provides remote control and real-time monitoring, allowing for proactive reactions to abnormalities or emergencies. It increases the overall efficiency of wastewater treatment, resulting in better water quality and less adverse environmental effects. This method may significantly improve wastewater management, creating a cleaner and healthier environment. © 2024 IEEE.

关键词： Wastewater treatment

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Ovarian Cancer Prediction using Machine Learning 3

Ovarian Cancer Prediction using Machine Learning

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3rd international conference on smart Generation computing, Communication and Networking, smart GENCON 2023

作者： Agnihotri, Yashika Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Ovarian cancer is the type of cancer that has the highest recurrence rate in women and poses a serious threat to women. Due to the lack of observable signs, this quiet invader often goes undetected in the beginning, leaving women susceptible to its sneaky development. Once it has advanced and its symptoms are obvious, it becomes arduous to treat. Machine learning is emerging as a ray of hope in the healthcare scene in the current era of rapid technological innovation. Machine learning provides a promising path for the early diagnosis and prediction of ovarian cancer by harnessing the power of algorithms and data analytics, perhaps turning the tide against this tough enemy. The dataset [17] comprises of 349 instances and 49 features. The goal here is to derive a model that helps in classification of instances into two classes: Benign Ovarian Tumors (BOT) and Ovarian Cancer (OC). Therefore, methodical machine learning algorithms are employed to detect ovarian cancer. Our research findings revealed that out of all the classifiers, the Random Forest Model has the highest accuracy (91.43 %). This approach is capable of accurately classifying BOT and OC and could have immense effect on diagnosis of patients, leading to better treatments and higher survival rate. © 2023 IEEE.

关键词： Classification Decision Tree Model KNN Logistic Regression Model Random Forest Model SGD Model and Voting Classifier Model SVM Model Used - Machine Learning XGB Model

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ADVANCED data PROCESSING METHODS IN AN intelligent HEALTH GATEWAY 16

ADVANCED DATA PROCESSING METHODS IN AN INTELLIGENT HEALTH GA...

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16th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th international conference on Connected smart Cities, CSC 2022, 7th international conference on Big data analytics, data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on Theory and Practice in Modern computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Lysmos, Mezui Eya'a Guy Hanoune, Mostafa Dept. of Computer Science University Hassan II of Casablanca Faculty of the Sciences Ben M'Sik Av Driss El Harti B.P 7955 Sidi Othmane Casablanca Morocco

ISBN: (纸本)9789898704429

In this article, we introduce advanced data processing methods in an intelligent gateway. These methods are techniques for optimizing, in a first stage, the storage space of the data of the system coming from the sensors in real time and, in a second stage, the bandwidth of the network for the transfer of the data from the local to the cloud or the remote server. Among these methods, we have first, centralized data processing that uses cyclical supervision at each detected data stream. This supervision is carried out thanks to a controller agent, which filters and segments the data according to their nature, their size and their sensitivity over regular time intervals. Second, random data processing, which consists of randomly processing the data sent by the sensors when the behavior of the compression node is stable. Third, dynamic targeted data processing that allows you to dynamically choose which node to query. During this approach, we used global variables to determine the limits of the volumes of data stored during a given period, to control the stopping and the resuming of data transfer. Finally, a comparative study of these methods has been highlighted to highlight the performance and efficiency of each. © MCCSIS *** rights reserved.

关键词： Internet of things

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Predictive analytics for Cybersecurity Using AI and ML: An Ensemble Approach 12th

Predictive Analytics for Cybersecurity Using AI and ML: An E...

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12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Dhage, Prashant Sharma, Shweta Patil, Milind S. Bhattacharya, Saurabh Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. D. Y. Patil College of Engineering and Innovation Talegaon410507 India Nagpur Campus Pune India Artificial Intelligence and Data Science Arya College of Engineering Jaipur India EnTC VIIT Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, advanced and proactive defense mechanisms are crucial. This study investigates the utilization of Artificial Intelligence (AI) and Machine Learning (ML) in predictive analytics to forecast and categorize various cyberattacks, with a specific focus on Distributed Denial of Service (DDoS) and Man-in-the-Middle attacks. The study utilizes a detailed Edge-Industrial Internet of Things (IIoT) dataset that represents actual network traffic situations. The proposed method combines Isolation Forest, Random Forest, and One-Class Support Vector Machine (SVM) to improve the accuracy of attack prediction using an ensemble approach. Isolation Forest is effective at identifying anomalies in datasets, Random Forest is strong at classification, and One-Class SVM is adept at identifying normal behavior patterns. These techniques work together to create a comprehensive and efficient defense system against various cyber threats. The predictive analytics model evaluation shows an accuracy rate of 98.23%, highlighting the system’s capability to classify incoming network traffic as normal or indicate an attack with precision. The True Positive Rate (TPR) reaches a notable 98.78%, demonstrating the model’s accuracy in detecting and predicting cyberattacks. The proposed ensemble method demonstrates a high level of accuracy and sensitivity, highlighting its potential practicality and reliability in real-world cybersecurity applications. The study enhances the development of sophisticated predictive analytics models for cybersecurity and emphasizes the importance of using AI and ML techniques, along with diverse datasets, to strengthen and adapt cyber defense mechanisms against changing cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AI and ML Cyber threat prediction Cybersecurity Edge-IIoT dataset Ensemble method Predictive analytics

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Cloud analytics: An Outline of Tools and Practices 2nd

Cloud Analytics: An Outline of Tools and Practices

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2nd international conference on intelligent and Cloud computing, ICICC 2021

作者： Kaur, Gunseerat Tiwari, Tejashwa Kumar Tyagi, Apoorva Lovely Professional University Punjab India

ISBN: (纸本)9789811698729

This paper projects an overview of how cloud-based analytical tools can provide for varied purposes of data aggregation and analytics. analytics-as-a-service is a forthcoming facility, extensively provided through multiple vendors. The aim is to elucidate which tools can serve better in what sort of data sources. As cloud computing has garnered much-needed growth in terms of data generation, there is a huge opportunity to explore the generated data. IT firms invest in creating, managing, and lending storages to handle this volume;it also challenges analyzing the data relevantly and effectively. It has called for an intelligent analysis of data and its storage in a sustainable and regulated environment. While various methods are employed to mine this heap of data, the industry is looking for newer technologies that can increase the rate at which they can analyze the data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Deriving data Modeling Requirements Using Topic Modelling in Cancer Care Context: The Case of the iCanViz Project

Deriving Data Modeling Requirements Using Topic Modelling in...

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2023 international conference on intelligent computing, Communication, Networking and Services, ICCNS 2023

作者： Mohammed, Mahmoud Atiani, Serin Rub, Reema Abu Al Kharbat, Faten F Odeh, Mohammad Princess Sumaya University for Technology Data Science Department Amman Jordan Al Ain University College of Engineering Abu Dhabi United Arab Emirates Cancer Care Informatics King Hussein Cancer Center Jordan University of the West of England United Kingdom Global Academy for Digital Health United Kingdom

ISBN: (纸本)9798350339291

The Development of a data model for the iCanViz cancer navigator, a smart cancer incidence analytics project, proved difficult due to the large body of resources to be analyzed. In this paper, we propose an approach to design, develop, and evaluate the data models for projects with large bodies of resources. The proposed approach utilizes topic modeling for data requirement discovery to analyze the semantic structures of thousands of documents and published articles and derive a reference conceptual data model for data strategy use-cases (DS-UCs) for cancer survival and deaths. Using this approach, we are able to process and analyze a great number of resources and map their automatically discovered topics into data model entities. Using the knowledge of a field expert, the derived model was evaluated and adjusted to arrive at a final model that captures the data requirements of the studied DS-UCs. The developed approach proved to be an effective benchmark for data modeling of a complex domain like cancer care, for which the validation of a domain expert is shown to be a critical element. © 2023 IEEE.

关键词： Diseases

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intelligent Optimization of Computer Image Processing Technology Analysis 3rd

Intelligent Optimization of Computer Image Processing Techno...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wei, Huayong Anhui Communications Vocational and Technical College Hefei China

ISBN: (纸本)9783031631382

intelligent optimization algorithm is an advanced computing technology, which simulates the biological evolution process in nature or the logical thinking of human beings to find a solution to the problem. In computer image processing, intelligent optimization algorithms are widely used, mainly in image enhancement, image restoration, image segmentation, feature extraction, image recognition and so on. intelligent optimization algorithms have developed rapidly, and many excellent algorithms with different characteristics have emerged, which have achieved good results in practical applications. Image analysis is the basis for realizing machine vision, including image enhancement, image fusion, image recognition, image tracking, image retrieval and many other technologies. It has a great demand in medicine, transportation, military, aerospace and other fields. In particular, the development of many industries and fields such as intelligent robots, smart medicine, and smart cities has brought many optimization challenges to image analysis, At present, image analysis based on swarm intelligence optimization algorithm has become an important research hotspot. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Image enhancement

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Performance Analysis of Clan Updated Grey Wolf Optimization (CU-GWO) based Deep Ensemble Technique for Identifying Sarcasm from Social Media data

Performance Analysis of Clan Updated Grey Wolf Optimization ...

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2023 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2023

作者： Sahu, Geeta Abakash Hudnurkar, Manoj Maharashtra Pune India Department of Information Technology and Data Science Mumbai India Maharashtra Pune India

ISBN: (纸本)9798350319200

Sarcasm is often considered as a prevalent linguistic element in online content that conveys strongly held subjectivity and viewpoints. The detection of sarcasm is a significant consideration over the dominant NLP appliances as sentiment analysis, which is utilizing for advertising and opinion mining is a precious tool for figuring out the people sentiments and attitudes. The proposed approach describes automatic detection of sarcasm as an ease classification of text challenges. This paper aims to develop a sarcasm detection model that categorizes words into either sarcastic or non-sarcastic expressions. Initially, the text input involves processing, here the stop word removal and tokenization process are carried out. Subsequently, various features like information gain, chisquare, mutual information, and symmetrical uncertaintybased features are extracted from the preprocessed data. The curse of dimensionality is a crucial issue, thereby the selection of optimal features is conducted by employing hybrid optimization model called Clan Updated Grey Wolf Optimization (CU-GWO). The optimal features are utilized for sarcasm detection by using ensemble technique. The ensemble techniques involve the classifiers such as NN, SVM, RF and DCNN. The final output of DCNN indicates whether the sarcasm is present. The effectiveness of the proposed approach demonstrated through diverse metrices. © 2023 IEEE.

关键词： DCNN ensemble technique preprocessing sarcasm detection

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