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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aier, Liaren Emani Bhowmick, Sreya Das, Barasha Boruah, Arpita Nath Goswami, Mrinal Programme of Computer Science and Engineering Faculty of Engineering Assam down town University Sankar Madhab Path Panikhaiti Gandhi Nagar Assam Guwahati India

ISBN: (纸本)9789819757022

In today’s digital age, the Internet is pivotal in bringing both convenience and security concerns. Individuals are vulnerable to unauthorized access and manipulation of their information by potential attackers. A notable cybersecurity threat revolves around injections, a method for introducing malicious code into web applications, creating vulnerabilities that attackers can exploit. Within the scope of this study, our attention has been directed toward a specific form of injection attack known as cross-site scripting (XSS). Following thorough research, our strategic direction involves adopting a bagging ensemble learning method for detecting XSS anomalies. The random forest classifier was employed as the chosen model in this approach. Considering all the essential comparison parameters, the proposed simulation results significantly improve the existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Deciphering Stem Cell Fate with an Integrative Multi-Omics Examination of Microenvironmental Dynamics

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SN computer science 2025年第1期6卷 1-14页

作者： Shanthanam, Radha Rammohan Selvam, Janani Vajravelu, Ashok Pradeep, T. Post Doctoral Fellowship (PDF) programme in Computer Science Engineering Lincoln University College Petaling Jaya Malaysia Faculty of Engineering Lincoln University College Petaling Jaya Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Kongu Engineering College Tamilnadu Erode India

The evidence-based literature on healthcare is akin to a vast universe, continually expanding with new discoveries and insights. In this dynamic landscape, the integration oftools and multi-omics approaches a beacon of hope, illuminating the intricate pathways that govern stem cell fate within the microenvironment. Still, figuring out how stem cell fate is determined is complicated and calls for a multifaceted understanding that cuts beyond traditional discipline lines. This review navigates through the evolutionary trajectory of AI integration in medicine, tracing its transformative impact on hematology and beyond. It explores the burgeoning field of AI applications in hematopoietic cell transplantation (HCT), from diagnosis to prognosis, heralding a new era of precision medicine. Through the lens of integrative multi-omics methodologies, the review delves into the molecular signatures intricately woven within the stem cell niche, deciphering the regulatory networks orchestrating stem cell fate decisions. Emerging technologies like single-cell multi-omics profiling and spatial transcriptomics offer newfound vistas to explore cellular heterogeneity and spatial organization within the stem cell niche, paving the way for transformative advances in regenerative medicine. Harnessing the power of AI and multi-omics methodologies, this review embarks on a quest to dissect stem cell fate with unparalleled precision, ushering in a new era of personalized therapeutics and regenerative interventions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cellular heterogeneity Hematology Molecular signatures Precision medicine Stem cell niche

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Intent-based Service Graph Selection for Cost-Effective Cloud Deployment 28

Intent-based Service Graph Selection for Cost-Effective Clou...

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Sabour, Sasan Ebrahimzadeh, Amin Soualhia, Mbarka Wuhib, Fetahi Glitho, Roch H. Telecommunication Service Engineering Research Lab Concordia University MontréalQC Canada Ericsson Research MontréalQC Canada Computer Science Programme University of Western Cape Capetown South Africa

ISBN: (纸本)9798331542399

Cloud computing offers a diverse range of virtualized services for users. However, selecting and composing these services to generate optimal service graphs that meet user requirements is complex because it requires an expert with extensive knowledge of the cloud applications and their service composition, to manually generate service graphs. This, however, cannot often guarantee that all the requirements are met. A potentially promising approach to address this complexity is to develop a system that enables cloud users to specify their intent and then to automatically select a service graph that meets all their requirements. In this paper, we address the problem of service graph selection while considering both functional and non-functional requirements derived from user intent. Our objective is to minimize the total deployment cost of the service graph in a data center network and to determine its placement within the distributed data center network. We formulate the problem as an Integer Linear Programming (ILP), taking into account the bandwidth and latency requirements of user intent. To solve this, we propose our Service Graph Selection (SGS) solution, which aims to achieve a near-optimal solution in a computationally efficient manner. Our results demonstrate that the proposed solution achieves a deployment cost that is only 4-6% larger than the lower bound of the optimal deployment cost. © 2025 IEEE.

关键词： Cloud platforms

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia computer science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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VR-Fi: Positioning and Recognizing Hand Gestures via VR-embedded Wi-Fi Sensing

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Hongbo Li, Xin Li, Jiachun Zhu, Haojin Luo, Jun Collaborative InitiativeInterdisciplinary Graduate Programme 639798 Singapore College of Computing and Data Science 639798 Singapore Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

Accurate gesture-based interactions are crucial for enhancing the immersive experience in VR (virtual reality) systems;they in turn necessitate gesture positioning and recognition in physical world. However, existing VR gesture recognition methods are predominantly vision-based, incurring high computational demands and raising privacy concerns. Meanwhile, Wi-Fi-based gesture recognition methods, deemed as promising complement to vision-based ones, typically lack gesture positioning capabilities. To this end, we propose VR-Fi, a gesture positioning and recognition system leveraging VR(-headset)-embedded Wi-Fi. To position gestures across different areas, VR-Fi innovates in a frequency-hopping bandwidth expansion (FHBE) technique to improve spatial resolution for locating a target. Additionally, VR-Fi innovates in neural models to process the FHBE-enhanced Wi-Fi CSI (channel state information) and enable the multi-task requirements of the joint positioning and recognition of hand gestures. Extensive experimental results demonstrate that VR-Fi achieves a positioning accuracy of 94.47%, a recognition accuracy of 92.13%, and a joint accuracy of 89.47%. © 2002-2012 IEEE.

关键词： Frequency hopping

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Integrating Imaging and Clinical Data for Parkinson's Disease Diagnosis using Deep Learning

Integrating Imaging and Clinical Data for Parkinson's Diseas...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： D. Divya Pravin R. Kshirsagar Tan Kuan Tak Quadri Noorulhasan Naveed Department of Computer Science and Engineering Jerusalem College of Engineering Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore Department of Computer Science and Engineering King Khalid University Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

That disorder affecting the nervous system, Parkinson's disorder can worsen with time and cause a host of health problems. Parkinson's disease diminishes cerebral function and may induce physical complications in senior individuals. Parkinson's disease can induce a range of disorders by impairing neurological in nature, behavioral, and physiological systems. Physical structures exhibit minimal damage at early diagnosis, complicating the prediction of Parkinson's illness in its initial stages based solely on physical observation. Recent studies on electroencephalogram (EEG) signs in the identification of Parkinson's illness have revealed significant aspects pertinent to early detection. Electroencephalography (EEG) is a multifaceted signal comprising several non-linear and non-stationary elements. Subsequently, an auto-encoder-based deep extraction of features method is employed to derive the spatial and temporal attributes from the four frequency partitions. Additionally, statistical and spectral attributes are obtained. The gathered attributes are utilized to analyze notable patterns associated with Parkinson's disease. Optimal weighted feature fusion is achieved through the precise adjustment of weights. The proposed DLB-LSTM (Densely Located Bidirectional Long Short Term Memory) employs weighted features so that we can identify PD. To impede the progression of Parkinson's disease, the algorithm autonomously learns from the signals and delivers targeted outcomes.

关键词： Deep learning Parkinson's disease Imaging Bidirectional long short term memory Feature extraction Nervous system Electroencephalography Physiology Informatics

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Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 104-122页

作者： Mandita, Fridy Ashari, Ahmad Wibowo, Moh. Edi Suryanto, Wiwit Department of Engineering Universitas 17 Agustus 1945 Surabaya Surabaya60118 Indonesia Programme of Computer Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Physics Universitas Gadjah Mada Yogyakarta55281 Indonesia

Volcanic eruption refers to a natural catastrophe on Earth that poses imminent danger to communities surrounding volcanoes. Therefore, ongoing monitoring of volcanic processes is crucial for effective analysis and observation of volcanic activities preceding an eruption. In response to this, the study presents a novel hybrid time series approach, integrated with machine learning techniques, to enhance the identification and classification of seismic events associated with volcanic eruptions. In this case, time series techniques, including STA/LTA, template matching, and autocorrelation, were implemented to facilitate the detection and classification process. The challenges, however, lie in addressing noise and ensuring accuracy in the analysis of seismic signals. To resolve this, a new hybrid time series method was proposed to improve signal analysis accuracy by integrating multiple time series techniques. In practice, the dataset was collected from Mount Merapi in Indonesia between 2019 and 2021, consisting of a compilation of seismic data categorized by event type, thus enhancing classification accuracy. On top of that, prior to implementing machine learning techniques for signal classification, the hybrid method was employed to efficiently remove noise, ensuring that genuine seismic events were clearly distinguished from spurious signals. Notably, the experimental learning rate was set at 0.01. The results demonstrated that the proposed hybrid method outperformed stand-alone time series techniques, achieving an accuracy of 0.93 to 0.95. This signifies the effectiveness of precise seismic event recognition and categorization, greatly enhancing the volcano monitoring system. Furthermore, the findings offer substantial improvements in the forecasting and risk mitigation associated with volcanic eruptions, hence, advancing reliable seismic analysis methodologies. Ultimately, the method enhances hybrid methods and machine learning for seismic event analysis and volcan

关键词： Volcanoes

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BoTNetShield: Intelligent IoT Security Framework using Convolution Neural Network

BoTNetShield: Intelligent IoT Security Framework using Convo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： A. Sivasundari Pravin R. Kshirsagar Tan Kuan Tak Shrikant V. Sonekar Department of Computer Science and Engineering Misrimal Navajee Munoth Jain Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore J D College of Engineering & Management Nagpur India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The "Internet of Things (IoT)" sector has grown in recent years, penetrating many different markets. The diversity and diminutive size of Internet-connected gadgets render them vulnerable to attacks. Improving and managing network forensic processes to avoid the various attacks, key logging, and denial of service becomes challenging in big and heterogeneous networks. Implementing high-end safety systems is challenging in IoT networks due to their variety and resource restrictions. Using deep learning, botnet detection is made better. It often requires a great deal of internet engagement. Internet of Things devices with little RAM have difficulties when using deep learning. This study use Convolutional Neural Networks (CNNs) for model training and Similarity Associated Feature Selection (SAFS) to decrease the dimensionality of smaller Internet of Things (IoT) traffic networks in order to effectively classify attacks in devices with limited memory. The effectiveness of botnet attacks is evaluated by first collecting datasets from the internet. By improving attack detection data from acquired datasets, data cleaning aids in eliminating unnecessary information. To enhance attack detection, SAFS cleans the data and then extracts efficient deep features. To classify risks to Internet of Things devices based on retrieved deep data, an efficient Recurrent Neural Network method is employed. Research evaluations compare proposed methods against existing ones in order to determine whether one is more effective.

关键词： Deep learning Training Recurrent neural networks Botnet Random access memory Feature extraction Safety Internet of Things Security Informatics

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Hybrid Heuristic Optimization of Deep Learning Models for Robust Plant Disease Detection

Hybrid Heuristic Optimization of Deep Learning Models for Ro...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization Informatics Optimization Monitoring

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Intent-Based Service Graph Selection for Cost-Effective Cloud Deployment

Intent-Based Service Graph Selection for Cost-Effective Clou...

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Sasan Sabour Amin Ebrahimzadeh Mbarka Soualhia Fetahi Wuhib Roch. H. Glitho Telecommunication Service Engineering Research Lab Concordia University Montréal QC Canada Ericsson Research Montréal QC Canada Computer Science Programme University of Western Cape Capetown South Africa

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Cloud computing offers a diverse range of virtualized services for users. However, selecting and composing these services to generate optimal service graphs that meet user requirements is complex because it requires an expert with extensive knowledge of the cloud applications and their service composition, to manually generate service graphs. This, however, cannot often guarantee that all the requirements are met. A potentially promising approach to address this complexity is to develop a system that enables cloud users to specify their intent and then to automatically select a service graph that meets all their requirements. In this paper, we address the problem of service graph selection while considering both functional and non-functional requirements derived from user intent. Our objective is to minimize the total deployment cost of the service graph in a data center network and to determine its placement within the distributed data center network. We formulate the problem as an Integer Linear Programming (ILP), taking into account the bandwidth and latency requirements of user intent. To solve this, we propose our Service Graph Selection (SGS) solution, which aims to achieve a near-optimal solution in a computationally efficient manner. Our results demonstrate that the proposed solution achieves a deployment cost that is only 4–6% larger than the lower bound of the optimal deployment cost.

关键词： Cloud computing Data centers Technological innovation Lower bound Costs Bandwidth Integer linear programming Computational efficiency Complexity theory

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