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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Saket Yadav, Dhananjay Sandeep Rao, P. Nishanthi, R. Gawali, Piyush P. Dhivrani, Rekha Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Cell devices have become increasingly popular in today's digital global. They may be becoming extensively used in many unique networks, including wireless ones. As such, it's vital to ensure that those gadgets are competently secured to protect them from potential protection breaches or malicious attacks. This paper examines the technologies to be had for investigating and enforcing the security of mobile devices in Wi-Fi networks, the contemporary protection answers available, and how those may be used to ensure that cell tool integrity is maintained. This paper also discusses how those solutions can be used to come across and respond to malicious safety threats. ultimately, it attempts to pick out capacity dangers associated with cellular devices and capability techniques to counter these threats. © 2023 IEEE.

关键词： adequately associated increasingly maintained technologies

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Harnessing Artificial Intelligence for High-Impact Data Science Applications

Harnessing Artificial Intelligence for High-Impact Data Scie...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Akhilendra Pratap Shreenidhi, H.S. Jain, Narendra Kumar Vanaja, T. Wanjale, Kirti H. Dehankar, Pooja Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India School of Engineering Ajeenkya D. Y. Patil University Pune India

ISBN: (纸本)9798350329773

The software of synthetic Intelligence (AI) has end up more and more widespread in each industry and academia as a powerful tool for transforming statistics into precious insights. AI is especially effective for facts technological know-how packages, permitting agencies to pick out styles, traits, and correlations inside huge datasets. By using leveraging AI for records technological know-how packages, agencies can advantage actionable insights which include predictive analytics, forecast destiny occasions, and optimize results. This paper provides an in depth overview of the usage of AI in statistics technological know-how, the advantages of the use of AI, and the challenges related to its utility. An overview of several excessive-effect facts technology applications is likewise supplied, consisting of gadget getting to know, virtual fitness, herbal language processing, predictive analytics, and pc vision. The potential packages of AI are colossal and blanketed as a part of a complete information technological know-how strategy;it is able to offer corporations with a massive competitive benefit. © 2023 IEEE.

关键词： Predictive analytics

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A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes 10

A Comparative Study of Machine Learning Techniques for Early...

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10th IEEE International Conference on Communications and Networking, ComNet 2023

作者： Alzboon, Mowafaq Salem Al-Batah, Mohammad Alqaraleh, Muhyeeddin Abuashour, Ahmad Bader, Ahmad Fuad Jadara University Faculty of Information Technology Irbid Jordan Arab Open University Faculty of Computer Studies Al-Ardiya industrial Area Kuwait Jadara University Faculty of Engineering Irbid Jordan

ISBN: (纸本)9798350381719

In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes dataset, this study attempts to evaluate the efficacy of several machine-learning methods for diabetes prediction. The collection includes information on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 percent, followed by the Random Forest method, with an accuracy of 76.30 percent. The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool. © 2023 IEEE.

关键词： Diagnosis

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

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IALS: Innovative Approach for Lung Segmentation Applying Artificial Intelligence and Deep Learning

IALS: Innovative Approach for Lung Segmentation Applying Art...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Hossain, Mohammad Alamgir Shahi, Fazal Imam Husain, Sadia Suliman, Abdelrazig Kamal, Mohammad Shahid Ahmad, Yasir Talukdar, Mohammad Mazedul Huq Martin, R. John Jazan University College of Engineering & Computer Science Department of computer Science Jazan Saudi Arabia Jazan University ELearning Center Jazan Saudi Arabia University of Bisha Faculty of Computers and Information Technology Department of Information Technology Saudi Arabia Jazan University College of Arts and Humanities Department of Foreign Languages Jazan Saudi Arabia

ISBN: (纸本)9798331513894

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE © 2025 IEEE.

关键词： Medical applications

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Smart Advertising Based on Customer Preferences and Manage the Supermarket 4

Smart Advertising Based on Customer Preferences and Manage t...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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A Data Analysis Method Using Orthogonal Transformation in a Reproducing Kernel Hilbert Space

A Data Analysis Method Using Orthogonal Transformation in a ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qu, Lingxiao Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350337020

We propose a data analysis method that combines the objectives of nonlinear principal component analysis and nonlinear discriminant analysis with the kernel method in a reproducing kernel Hilbert space. This method addresses nonlinear data analysis problems in high-dimensional spaces, specifically the reproducing kernel Hilbert space, through the use of the kernel trick. Our proposed method can be considered as a semi-supervised data analysis approach. We evaluate our proposed method using various kernel functions and datasets, both visually and quantitatively. The evaluation results demonstrate that our proposal outperforms kernel principal component analysis and generalized discriminant analysis in terms of classification performance. This indicates the advantages and originality of our proposed method. Furthermore, we analyze and discuss our findings based on the evaluation results, and highlight potential areas for further research and future work related to our proposal. © 2023 IEEE.

关键词： Data Analysis Method Data Science Kernel Method Linear Discriminant Analysis Principal Component Analysis

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Autonomous Learning of Fuzzy Recurrent Neural Networks for Human Activity Recognition

Autonomous Learning of Fuzzy Recurrent Neural Networks for H...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Liang, Shuai Bing Loo, Chu Kiong Sabri, Aznul Qalid Md Wong, Kok Wai Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Malaysia School of Engineering and It Murdoch University MurdochWA Australia

ISBN: (纸本)9798350303780

In this article, an Autonomous Fuzzy Echo State Network (AFESN) algorithm is being proposed. This algorithm combines Autonomous Learning Multi-Model Systems and Echo State Network (ESN) to perform Human Activity Recognition on the Heterogeneity Human Activity Recognition HHAR dataset. We incorporate the structural information of the dataset into the ESN by using the Takagi-Sugeno (TS) models/rules concept. The process of obtaining the structural information in the dataset was fully automated by the unimodal density based membership function, which then acts as the antecedent part. Subsequently, a consequent part composed by ESN was used to replace the affine models commonly used in TS models. The final model predictions are inferred by the fuzzy-weighed mechanism in a new fuzzy rule composed of multiple ESNs. Since our algorithm is fully automatic and does not require any prior knowledge, its use in online learning scenarios is adequately applicable. Lastly, the effectiveness of the algorithm is tested using the HHAR dataset and an accuracy of 42.9% was obtained. Compared with the performance of recent Autonomous Learning Multi-Model Systems (19.6%), our algorithm has gained a significant improvement. © 2023 IEEE.

关键词： Membership functions

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