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9th International Conference on Smart City Applications, SCA 2024

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M’Sik Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

ISBN: (纸本)9783031886522

Breast cancer has emerged as a significant cause of female mortality globally, underscoring the critical need for early detection and diagnosis. Machine Learning, as a cutting-edge technology, holds immense promise in addressing healthcare challenges, including breast cancer detection. Despite advancements in technology, accurately discerning malignant breast masses from benign ones remains a complex and challenging task in medical research. Feature selection techniques play a vital role in this endeavor, as they aim to identify the most pertinent and informative features from complex datasets, thus improving the accuracy and reliability of models for detecting breast cancer. Numerous recent studies have emphasized the efficacy of metaheuristic algorithms in optimizing various problems across different domains, with particular focus on the Whale Optimization Algorithm (WOA). This paper focuses on evaluating the effectiveness of different feature selection (FS) approaches, such as WOA-based Feature selection, Recursive Feature Elimination, and SelectFromModel, in conjunction with diverse machine learning approaches for detecting breast cancer. The study assesses methods, including Support Vector Machine, XGBoost, Decision Tree, and Logistic regression using performance evaluation measures including, recall, F1-score, accuracy, area under the curve (AUC), precision, and feature removal percentage. Notably, FS based WOA combined with XGBoost model demonstrated outstanding performance measures, with an accuracy rate of 99.4%, an F1-score of 99.4%, 100.0% precision, 98.8% recall, and a 99.4% AUC. These findings contribute valuable insights into enhancing breast cancer detection accuracy and facilitating early intervention strategies, thereby potentially improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Breast Cancer Classification Feature Selection Machine Learning Whale Optimization Algorithm

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Enhancing Query Expansion with ML-SSO: A Machine Learning, Semantic, Statistical, and Ontological Framework

Enhancing Query Expansion with ML-SSO: A Machine Learning, S...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Surabhi Solanki Seema Verma Pulakesh Roy Sachin Kumar Rajib Banerjee Ajay Prasad Department of Computer Science & Engg Banasthali Vidyapith Tonk India Department of Electrical & Electronics Engineering Education NITTTR Bhopal India Department of Computer Science & Engineering Brainware University Kolkata India Departmnet of Computer Science & Engneering Parul University Vadodara India School of Computer Science UPES Dehradun India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This paper presents a Machine Learning -Semantic Statistical and Ontological query expansion approach to enhance the performance of information retrieval systems. The proposed methodology ML-SSO looks to combine the strengths of each technique to obtain relevancy and accurately expanded queries. In this, pre-trained word-embed models were used for contextually relevant term expansions, and domain-specific ontologies were used to provide accurate and custom query expansions. Large-scale experiments conducted on benchmark datasets demonstrated that the ML-SSO outperformed traditional ones in MAP, Precision at k (P@10), Recall, and F-measure scores. These results make clear that the ML-SSO query expansion method improves the relevance and accuracy of the Retrieved Document. Future work will be directed towards optimizing the scalability, incorporating dynamic ontology driven into it, and using other advanced models in machine learning to further increase the performance of the system.

关键词： Technological innovation Accuracy Scalability Semantics Machine learning Ontologies Benchmark testing Information retrieval Context modeling

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Privacy-Preserved and Secured Communication Model via Two-Phase Authentication Process for VANET System

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SN computer science 2025年第3期6卷 1-12页

作者： Bijalwan, Akhilesh Purohit, Kamlesh Chandra Jasola, Sanjay Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand Dehradun 248002 India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun 248002 India

Intelligent Transportation Systems (ITS) have been revolutionised by the growth of Vehicular Adhoc networks (VANETs). The exploration of fair and secure communication between vehicles is studied. Prior studies have stated that the authentication and privacy parameters in the data-sharing process are the major obstacles that need to be focussed on during transmission. This research tackles critical challenges in Vehicular Adhoc Networks (VANETs) by introducing an enhanced Geographic Secured Routing Protocol (E-GSRP) designed to improve authentication, ensure privacy, and detect Sybil attacks. The proposed solution features a two-phase authentication model consisting of the Reliability Checking Scheme (RCS) and the Distance Bounding Scheme (DBS). The RCS evaluates vehicle reliability using geographic location data, while the DBS further verifies authenticity by analyzing proximity and movement consistency. By leveraging the geographic routing of both current and neighboring vehicles, the protocol establishes a secure and reliable communication framework within the VANET environment. By taking the previous vehicles' feedback, the vehicles' behaviour deviations are observed and seen—experiments conducted in the Network Simulator-2 (NS-2) tool to characterise the deployed vehicles. The achieved results stated that the proposed E-GSRP has given better outcomes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distance Bounding Scheme (DBS) Geographic Secured Routing Protocol Intelligent Transportation Systems (ITS) Iterative Support Vector Machine (ISVM) Reliability Checking Scheme (RCS) Sybil attack

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A Comprehensive Von Willebrand Disease Awareness and Support Chatbot for Senegalese Communities

A Comprehensive Von Willebrand Disease Awareness and Support...

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International Conference on Information Networking

作者： Keerthi Sai Akuthota C Kishor Kumar Reddy Mohammed Shuaib Shadab Alam Faisal Alshanketi Aashritha Reddy Kyatham Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

A lack of the protein required for healthy blood coagulation results in Von Willebrand disease (VWD), a blood condition. The absence of readily available, thorough information to assist individuals with VWD in managing their illness is one of their biggest problems. Many patients believe they lack access to all the necessary resources, which is made worse by language and technology literacy challenges, especially in places like Senegal, where French and Wolof are the main languages. Senegal is a small coastal African country. Senegalese are made up of at least six ethnic groups, with the Wolof being the largest. With a particular focus on the Senegalese community, creating an educational intelligent chatbot for VWD sufferers seeks to address these issues by improving literacy and filling in knowledge gaps. Users can communicate with the system regardless of their technological expertise thanks to this chatbot’s use of speech recognition and machine learning technologies to provide precise, culturally relevant, and understandable information in both French and Wolof. The chatbot is intended to significantly improve the quality of life for VWD patients, especially in Senegal, by providing dependable, current materials in a safe online setting, equipping them with the knowledge they need to manage their illnesses properly. Through an inventive and user-friendly platform, the chatbot seeks to address the pressing need for more education and awareness of VWD, enhance illness management, and offer reliable educational resources. With the use of cutting-edge technology, the chatbot makes sure that information is presented in an approachable and culturally appropriate way, which facilitates patients’ comprehension and use of the advice in their everyday lives and, eventually, improves their capacity to manage VWD.

关键词： Proteins Sensitivity Education Sea measurements Speech recognition Machine learning Pressing Chatbots Reliability Diseases

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A Novel Super Constellation Design Paradigm

A Novel Super Constellation Design Paradigm

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IEEE Conference on Wireless Communications and Networking

作者： Thrassos K. Oikonomou Dimitrios Tyrovolas Sotiris A. Tegos Panagiotis D. Diamantoulakis Panagiotis Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the rapidly advancing field of sixth generation (6G) wireless networks, where the achievement of ultra-high data rates and energy efficiency is crucial, this study introduces the concept of super constellations as a solution to meet these pressing requirements. However, when higher-order modulations are employed, the system becomes increasingly susceptible to Gaussian phase noise (GPN), resulting in significant performance degradation. To address this issue, we propose a novel modulation scheme, called super amplitude phase shift keying (SAPSK), which is specifically designed to improve resilience against GPN. Furthermore, recognizing the critical need for efficient detection methods in high-order constellations, we present a detection framework that is not only capable of mitigating the detrimental effects of GPN, but also provides a foundation for the development of low-complexity detection algorithms. Specifically, we introduce the generalized polar distance detector (GPD-D), a detection metric that approximates maximum likelihood detection (MLD) in GPN-affected channels while introducing structured decision regions. Building on the GPD-D, SAPSK formulates a hexagonal lattice that provides improved energy efficiency and is further supported by a detection algorithm with Ό (1) complexity, ensuring fast and accurate symbol detection. Finally, the superiority of SAPSK is demonstrated through extensive numerical simulations, which show that SAPSK has significant advantages in terms of symbol error probability (SEP) over other conventional modulation schemes.

关键词： Phase noise Maximum likelihood detection Phase shift keying Wireless networks Symbols Detectors Pressing Energy efficiency Detection algorithms Resilience

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Path and Trajectory Planning in Unknown Tracks Using Pixy2.1 Camera for Autonomous Racecars

Path and Trajectory Planning in Unknown Tracks Using Pixy2.1...

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Control, Automation, and Instrumentation (IC2AI), International Conference on

作者： Mostafa Kassem Ghadi Abedulhadi Joe Azar Naseem Daher Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon Department of Electrical and Computer Engineering Department of Mechanical Engineering American University of Beirut Beirut Lebanon

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

Maintaining maximum allowable speeds throughout a racing track, especially at corners, is essential for competitive performance in autonomous racing. This research integrates computationally-efficient perception and path- and trajectory-planning algorithms, which are suitable for real-time deployment on limited hardware. The algorithms aim is to achieve the global-minimum lap time on an unknown race track by minimizing the summation of locally-minimum times at each track section using a Pixy2.1 camera, which is a fast vision sensor. By processing the vector outputs of the Pixy2.1 camera, smooth track right and left lanes are generated and an optimal race line that minimizes curvature is generated, and an adaptive velocity profile is developed to reach maximum allowable speed at each waypoint along the race line. Experimental testing and validation were conducted on the MR-BUGGY3-KIT car hardware equipped with an RDDRONE-FMUK66 NXP microcontroller. Testing demonstrated the system's ability to generate smooth lane boundaries from the Pixy2.1 camera vectors. Furthermore, the curvature profiles effectively captured the geometric characteristics of the track, facilitating efficient path and trajectory planning while minimizing curvature.

关键词： Trajectory planning Tracking Microcontrollers Wheels Cameras Real-time systems Hardware Vectors Automobiles Testing

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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AI-Based Geo Intelligent System for Attack Prediction and Virtual Simulation

AI-Based Geo Intelligent System for Attack Prediction and Vi...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Shruthi, N. Kavya Yadav, Ajit Kumar Singh North Eastern Regional Institute of Science and Technology Department of Computer Science and Engineering Arunachal Pradesh Nirjuli India

ISBN: (纸本)9798331527495

One of the blooming technologies of AI is the metaverse, The Metaverse is a virtual environment in which users may interact with virtual things and with one another. The Metaverse can be utilized in Military GeoIntelligent systems to build a virtual training environment, allowing Defence personnel to practice in realistic circumstances without the need for costly physical resources. The military personnel can practice in a virtual training environment using the Metaverse that can be created with realistic scenarios. This can aid in enhancing their abilities, judgment, and quickness of response. For instance, pilots can practice flying in various weather conditions, soldiers can hone their tactical skills, and cyber security officials can practice responding to cyberattacks. In this paper, we have focused on AI-based technologies combined with the geo intelligence and metaverse which can jointly help in defence to model and analyse complex geographic situations, allowing defence specialists to explore numerous situations and evaluate the effectiveness of different strategies before deploying resources, with the goal of improving situational awareness, response times, and decision-making capabilities across all security-related agencies. The satellite images obtained from the geo satellites give a brief description of a variety of different types of maps and visualizations, including topographic maps, vegetation maps, and thermal maps. These types of satellite imagery include infrared images, the locations of water bodies, planes, plateaus and a number of other surface structures of earth. The imagery helps in identifying the loopholes of spotting the enemy hiding locations and the risky regions of exposure of the native soldiers. The AI model can identify such locations and predict the possible threat locations. When this kind of model is deployed in the metaverse, it can help the soldiers in being trained for war zone like situations. The predictive outcome can hand

关键词： Virtual environments

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Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning

Label Consistent Generalized Adaptive Weighted Recursive Lea...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Mohadeseh Yousefi Yashar Naderahmadian Soosan Beheshti Department of Electrical and Computer Engineering University of Guilan Rasht Iran Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class.

关键词： Training Dictionaries Accuracy Sparse approximation Simulation Supervised learning Training data Machine learning Cost function Classification algorithms

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Optimization of Multi-Hop Wireless Routing with Grey Wolf Optimizer

Optimization of Multi-Hop Wireless Routing with Grey Wolf Op...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Najem Sirhan Manel Martinez-Ramon Computer Science Department University of Petra Amman Jordan Electrical and Computer Engineering Department University of New Mexico Albuquerque New Mexico USA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In modern wireless networks, multi-hop routing has emerged as a critical mechanism to extend coverage and increase the network's flexibility. Optimizing the routing process—deciding how data flows from a source node to a destination node—is a challenging task due to constrained link capacities, node interconnectivity, and dynamic network topologies. This paper presents a novel approach for optimizing multi-hop wireless routing using the Grey Wolf Optimizer (GWO). GWO is adapted to optimize flow-conservation and capacity constraints through a penalty-based mechanism, ensuring only feasible flows are rewarded. Results show that GWO identifies strong routing solutions and converges reliably to near-optimal flows, as evidenced by flow distributions across repeated trials. These findings affirm that nature-inspired algorithms such as GWO can effectively cope with the complexity and variability inherent in multi-hop wireless networks, delivering high-quality routing solutions that balance exploration of the search space with exploitation of promising configurations.

关键词： Solid modeling Network topology Wireless networks Spread spectrum communication Interference Routing Throughput Cost function Reliability Convergence

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