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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Efficient Capsule Based Neural Network for Detection of Grape Plant Leaf Disease and Disease Stages

An Efficient Capsule Based Neural Network for Detection of G...

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Emerging systems and Intelligent computing (ESIC), International Conference on

作者： N.Vasudevan S.Praveenkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Plant infections destroy and impair the quality of crops, and the pesticides used to treat them pollute the soil, rendering it unfit for planting. Image processing and deep learning technologies may be used to identify disease spots on grape leaves. The detection's precision and efficiency, on the other hand, remain problems. In this paper, the performance of a few well-known CNN models implemented utilizing transfer learning, such as ResNet18, VGG16, and GoogleNet were compared with capsule network. The local entity characteristics are first found by the Capsule layers. For the purpose of collecting aggregate data like "disease type" and "disease stage," the capsule layer makes use of geographic information and the frequency of local-level characteristics. Colored pictures of strong and unhealthy leaves were taken from the public dataset and then used to train the models. The proposed work focused on the novelty concept of identifying the stages of diseases. The Capsule based classification technique can give competitive benefits with an effective classification of accuracy and stages of leaf disease related to other models.

关键词： Deep learning Plant diseases Accuracy Image processing Pipelines Neural networks Transfer learning Soil Rendering (computer graphics) Diseases

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Augmenting the Proficiencies of GANs Through Modernizing Agriculture and Healthcare

Augmenting the Proficiencies of GANs Through Modernizing Agr...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Jothi T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Nowadays, generative models with deep learning have a focus and give enormous success in various real-time experience domains. Deep learning (DL) is a subdivision of machine learning that is inspired by the structure and function of human brain activity, known as artificial neural networks. It is a data-driven technique that achieves the best accuracy while using a large number of input samples. Generative Adversarial Networks (GANs) are a robust class of neural networks used for unsupervised learning under DL categories. GAN is one of the most influential architectures demonstrating remarkable success in generating high-quality data. The reusable feature learning process from a huge number of unlabelled data paves the way for research. This survey article discusses an overview of the GAN model, such as the original GAN, Conditional GANs, and resolution GANs, along with their specific adaptations for various tasks. Additionally, we examine the situations faced by Generative Adversarial Networks depending on various applications such as image and video generation, medical image analysis, text-to-image synthesis, agriculture, and creative industries such as music and art. This survey explores diverse applications of recently developed frameworks and benchmark datasets used for development, along with their architecture and metric evaluations.

关键词： Deep learning Surveys Measurement Image analysis Accuracy Generative adversarial networks Agriculture Real-time systems Medical diagnostic imaging Farming

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IDS: Robust Network Intrusion Detection via Temporal Contrastive Graph Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1475-1486页

作者： Wu, Cong Sun, Jianfei Chen, Jing Alazab, Mamoun Liu, Yang Xiang, Yang Wuhan University School of Cyber Science and Engineering Wuhan430072 China Singapore Management University School of Computing and Information Systems Bras Basah Singapore188065 Singapore Charles Darwin University College of Engineering IT and Environment DarwinNT0810 Australia Nanyang Technological University College of Computing and Data Science Jurong West Singapore639798 Singapore Swinburne University of Technology Department of Digital Research MelbourneVIC3122 Australia

In the era of zero trust security models and next-generation networks (NGN), the primary challenge is that network nodes may be untrusted, even if they have been verified, necessitating continuous validation and scrutiny. Effective intrusion detection systems (IDS) are crucial for continuously monitoring network traffic and identifying potential threats. However, traditional IDS approaches often struggle to keep pace with evolving threats, requiring extensive supervised training on labeled datasets. This limitation leads to high false positive rates, low detection accuracy, and a failure to provide real-time detection, thereby undermining the security of NGNs. This paper proposed the first self-supervised learning-based IDS, designed on temporal contrastive graph neural network (GNN), namely TCG-IDS. It innovatively integrates three contrastive learning strategies: temporal contrasting to capture temporal dependencies, asymmetric contrasting to account for the diverse interactions within network data, and masked contrasting to enhance the learning of node representations by masking parts of the data during training. Performance evaluation was conducted on two publicly available network traffic datasets, NF-CSE-CIC-IDS2018-V2 and NF-UNSW-NB15-V2. TCG-IDS achieved a balanced accuracy of 99.48% and 91.48% on two datasets respectively, significantly outperforming state-of-the-art graph learning models. In multi-class detection, TCG-IDS attained a mean false positive rate of 4.15% and 3.34% on the two datasets respectively. Besides, it exhibits high efficiency with its running time of 0.37s and 0.51s on the two datasets to predict per batch of 100 samples. Results highlight the effectiveness and efficiency of TCG-IDS in accurately detecting various types of network intrusions. This work significantly advances the field of network intrusion detection via self-supervised temporal graph learning, offering a promising solution for future network security systems. © 2005-2012

关键词： Network intrusion

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Semantic Segmentation of Satellite Imagery Using Optimized U-Net Model 1st

Semantic Segmentation of Satellite Imagery Using Optimized U...

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1st International Research Conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Kavipriya, J. Vadivu, G. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9783031823855

The classification of land cover is crucial due to increasing demands and population expansion. Segmentation of the terrain is utilized in environmental monitoring to accurately identify and delineate areas of agriculture, buildings, and water bodies. Satellite imagery is quite valuable for the purpose of segmentation. Deep learning models offer automated and precise segmentation of satellite imagery. The U-Net architecture is widely employed as an encoder-decoder structure for the purpose of segmenting medical and remote sensing images. This study investigates the process of dividing satellite images into segments using an optimized U-Net model. Our model utilizes improved equal weights to calculate loss functions. In addition, we have implemented a dropout layer for each convolutional block in order to decrease the computational time required for feature extraction in the encoder layer of the optimized U-Net model. Our study examines the Dubai dataset, which has six distinct classes. The optimized U-Net model we propose achieves an accuracy of 81.5% and Intersection over Union (IoU) values of 61.7% for segmenting the dataset. These metrics are acquired with a reduced number of epochs, hence decreasing the computational expense. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： S.P. Maniraj R Thamizhamuthu Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78 % . These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation.

关键词： Accuracy Sensitivity Attention mechanisms Telemedicine Melanoma Sensitivity and specificity Skin Pattern recognition Lesions Reliability

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Performance Evaluation of Intrusion Detection systems on the TON_IoT datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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GENSIMO - A Generic Framework for Modelling Social Insurance systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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