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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Mhsnhasan, Muntader Sowmya, M. Prabhakaran, M.V. Ghamya, K. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively. © 2025 IEEE.

关键词： Principal component analysis

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An Integrated Edge Computing Framework for Enhancing Cyber-Physical System Security through Network Traffic Anomaly Detection in Industrial IoT

An Integrated Edge Computing Framework for Enhancing Cyber-P...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Abdildayeva, Assel L.N. Gumilyov Eurasian National University Faculty of Information Technology Astana Kazakhstan Hazara University Department of Computer Science and Information Technology Mansehra21300 Pakistan Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan

ISBN: (纸本)9798331542603

With the expansion of the Industrial Internet of Things (IIoT), the protection of Cyber-Physical System (CPS) is worth consideration. This research presents an integrated edge computing framework that aims at enhancing security by improving network traffic anomaly detection. It is done based on three advanced anomaly detection algorithms, namely Principal Component Analysis (PCA), isolation forest, and autoencoder. PCA is applied to the data to bring it in a compressed form where one is able to detect the outliers by having the most important features of the data extracted. The isolation forest algorithm, which was applied with the contamination factor of 0, is depicted below: 1, is used for the detection of the anomalies in the traffic, and, in fact, it was able to categorize several threats. However, the autoencoder model relies on reconstruction loss to decide if something is wrong with the data, with the 95th percentile as the standard for identifying activities that are odd. Edge computing increases the effectiveness of a framework for real-time anomaly detection and mitigation. The objective of this research is to evaluate PCA, isolation forest, and autoencoder methods and the outcome on the feasibility of the methods to enhance the security of IIoT systems. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Innovative Deep Learning Techniques for Solving Complex Computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Deep Learning Strategies for Multiclass Skin Disease Classification 5th

Deep Learning Strategies for Multiclass Skin Disease Classi...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Venkat, Nakka Lokeswara Satya Tejasri, Masina Naga Vijaya Uppalapati, Padma Jyothi Rao, V. V. R. Maheswara Sripada, V. S. S. Lakshmi Murty, P. Sita Rama Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of CSE Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534201 India Andhra Pradesh Bhimavaram534201 India

ISBN: (纸本)9783031770746

Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of VGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Dermatitis

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Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

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International Journal of Business intelligence and data Mining 2023年第1期24卷 1-24页

作者： Nirbhav Malik, Anand Maheshwar Prasad, Mukesh Department of Geography Delhi School of Economics University of Delhi Delhi India Swami Shraddhanand College University of Delhi Delhi India TGT Computer Science Directorate of Education Delhi Government Delhi India Faculty of Engineering and Information Technology School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. Information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Landslides

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Review of Machine Learning and Deep Learning Techniques for Medical Image Analysis

Review of Machine Learning and Deep Learning Techniques for ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Saniya Saratkar Rohini Raut Trupti Thute Aarti Chaudhari Gaitri Thakre Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The field of dynamic medical imaging research is witnessing a tremendous growth in machine and deep learning methods. At the moment, significant efforts are being made to improve medical imaging applications by applying these algorithms to identify errors in disease diagnosis systems that might lead to incredibly unclear medical interventions. In medical imaging, machine learning and deep learning algorithms play a significant role in predicting early illness signs. Algorithms for deep learning (DL) and machine learning (ML) provide viable ways to get beyond these restrictions. This research investigates the possibilities of ML and DL for medical picture analysis. In this research study, the foundational ideas of machine learning and deep learning, its uses in medical imaging, such as picture segmentation, reconstruction, and classification, as well as comparisons of their efficacy with more conventional techniques are discussed. This work provides an overview of medical imaging in machine and deep learning technologies to assess various illnesses

关键词： Deep learning Machine learning algorithms Reviews Transforms Prediction algorithms Planning Medical diagnosis Internet of Things Medical diagnostic imaging Monitoring

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

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Computers, Materials & Continua 2024年第11期81卷 3395-3412页

作者： Arar Al Tawil Laiali Almazaydeh Doaa Qawasmeh Baraah Qawasmeh Mohammad Alshinwan Khaled Elleithy Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan College of Engineering Abu Dhabi UniversityAbu DhabiAl AinP.O.Box 1790United Arab Emirates Faculty of Artificial Intelligence Al-Balqa Applied UniversitySalt19117Jordan Department of Civil and Construction Engineering Western Michigan UniversityKalamazooMI 49008USA MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science and Engineering University of BridgeportBridgeportCT 06604USA

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as *** study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing *** study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron *** best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching *** study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

关键词： Attacks email phishing machine learning security representations from transformers(BERT) text classifeir natural language processing(NLP)

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Smart Energy Management System Using Non-intrusive Load Monitoring

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SN Computer science 2022年第2期3卷 1-11页

作者： Deshpande, Riya Hire, Shubhankar Mohammed, Zakee Ahmed Electronics and Telecommunication Pune Institute of Computer Technology Maharashtra Pune India Artificial Intelligence and Data Science Chhatrapati Shahu College of Engineering Maharashtra Aurangabad India

Energy Management is a problem faced by many around the world. The ever-rising demand for energy is putting a strain on the worldwide resources. Additionally, during the pandemic, it was observed that there was a lot of discrepancy in the electricity bills. To do our part in addressing the issue, the combination of Internet of Things (IoT) and Machine Learning (ML) has been used in creating a solution which will help measure, monitor and visualize daily energy consumption of a household. Additionally, using the concept of Non-Intrusive Load Monitoring (NILM), a single hardware setup can be used to measure the energy consumption of each appliance in the household. This hardware setup with the use of certain ML algorithms like Factorial Hidden Markov Model (FHMM) and Combinatorial Optimization (CO) disaggregates the combined household energy readings to device specific values. These values then get sent to a cloud database and are presented to the user through a Dashboard like visual interface. Therefore, the system in whole offers a combined solution to the user with minimal setup and cost to give a generic idea based on the energy usage, pattern, consumption. Such monitoring and systems can help efficient and responsible energy usage and can go a long way in ensuring sustainability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Energy management Internet of things Machine learning Non-intrusive load monitoring

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Enhancing Residential Security with AI-Powered Intrusion Detection Systems

Enhancing Residential Security with AI-Powered Intrusion Det...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Jeneetha Jebanazer, J. Prabha, B.Vishnu Yasotha, B. Jaisudha, J. Senthilkumar, C. Pandi, V.Samuthira Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (纸本)9798350313987

Using Intrusion Detection Systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today. © 2023 IEEE.

关键词： Anomaly Detection artificial intelligence Convolutional Neural Network Home Protection Intrusion Detection Sensor Integration

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