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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rai, Kajal Kumar, Pawan Gl Bajaj Institute of Technology and Management Dept. of Mca Uttar Pradesh Greater Noida India Lovely Professional University School of Computer Applications Punjab Phagwara India

ISBN: (纸本)9798331508685

This work presents an automated Resume Analyzer system to streamline the initial resume screening process. Designed to alleviate recruiters' workloads, the system extracts key resume details - such as name, contact information, experience, and qualifications - without manual input. This system leverages Natural Language Processing (NLP) and text mining. A novel scoring mechanism evaluates resumes based on the presence of essential elements like declarations, interests, and skill sets. This web tool provides a resume score which is used by organization and applicants to review about it. This tool allows recruiters to assess resumes in PDF with a rating rapidly, saving them both time and effort. The SVM algorithm outperformed kNN, Naïve Bayes, Decision Tree and Random Forest. © 2025 IEEE.

关键词： Random forests

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Deep Learning-Based Spectrum Sharing for Dynamic Resource Allocation in 6G Cognitive Radio Networks 4

Deep Learning-Based Spectrum Sharing for Dynamic Resource Al...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Venu, Nookala Kukreti, Kamlesh Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331530402

We conduct an extensive study on deep learning-based spectrum sharing to resolve dynamic resource allocation in 6G cognitive radio networks in this paper. The approach uses modern machine learning models to optimize spectrum usage, increase prediction accuracy of spectrum availability, and reduce interference among the primary and secondary users. We need to evaluate the performance of deep learning algorithms against the traditional spectrum allocation methods in simulation. In the paper, regular quality of service metrics such as throughput distribution and fairness among users are demonstrated to be much improved with these results, where both latency and energy consumption have been cut down dramatically. This work demonstrates the paradigm-shifting role of deep learning in spectrum management for 6G networks, pointing towards intelligent and effective resource allocation mechanisms in tomorrow's communications. © 2025 IEEE.

关键词： Cognitive radio

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InterACT: Enhancing Human-AI Interaction 2

InterACT: Enhancing Human-AI Interaction

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

作者： Katta, Jithendra Kodumuru, Nikhil Reddy Katta, Swathi School of Engineering and Applied Sciences The George Washington University WashingtonDC United States Vignana Bharathi Institute of Technology Computer Science & Engineering dept. Hyderabad India

ISBN: (纸本)9798331505745

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools. © 2025 IEEE.

关键词： Object detection

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Advancing Fake News Detection: From Traditional Machine Learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning

Estimating the Number and Locations of Boundaries in Reverbe...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Arikan, Toros Chackalackal, Luca M. Ahsan, Fatima Tittel, Konrad Singer, Andrew C. Wornell, Gregory W. Baraniuk, Richard G. Dept. of ECE Rice University HoustonTX United States Dept. of Computer Science Rice University HoustonTX United States Dept. of ECE State University of New York at Stony Brook Stony BrookNY United States Dept. of EECS Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9798350368741

Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which outperformed the state-of-the-art, both in simulation and in real-life experimental settings. A limitation of this method was that some prior information had to be provided by the user on the number and locations of the reflective boundaries, and that its neural networks had to be re-trained accordingly for different environments. Utilizing more advanced neural network and time delay estimation techniques, the proposed improved method no longer requires prior knowledge the number of boundaries or their locations, and is able to estimate two-dimensional environments with one or two boundaries. Future work will extend the proposed method to more boundaries and larger-scale environments. © 2025 IEEE.

关键词： Convolutional neural networks delay estimation localization underwater acoustics

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Hybrid Feature Engineering and Sampling Strategies for Enhanced Diabetic Retinopathy Classification 3

Hybrid Feature Engineering and Sampling Strategies for Enhan...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Kumar Khan, Ajoy Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Ophthalmologist Aizawl India

ISBN: (纸本)9798331523893

This study presents a machine learning approach for Diabetic Retinopathy (DR) classification, integrating advanced preprocessing, feature extraction, and adaptive sampling. Preprocessing techniques, including CLAHE, gamma correction, and noise reduction, standardize image quality while preserving anatomical details. Class imbalance is addressed with ADASYN, generating synthetic samples in sparse regions for balanced class representation. Feature extraction focuses on clinical markers like vascular density, microaneurysms, exudates, and texture descriptors such as GLCM and LBP. Recursive Feature Elimination (RFE) refines features for better interpretability and reduced computational load. This binary classification framework achieves 92.91% accuracy on test data and a cross-validated ROC AUC of 0.994, demonstrating the effectiveness of hybrid feature engineering and intelligent sampling in DR detection. © 2025 IEEE.

关键词： Diabetic Retinopathy Fundus Image Image Processing Machine Learning Medical Image Diagnostics

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Short-term renewable energy uncertainties and inertia consideration in power system generation expansion planning

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Energy Reports 2025年 13卷 5978-5992页

作者： Oladimeji Joseph Ayamolowo K. Kusakana Dept. of Electrical Electronic and Computer Engineering Central University of Technology Free State South Africa

In modern power systems, renewable energy (RE) sources are increasingly integrated to reduce reliance on fossil-fuel-based generation. However, the uncertainties and lack of rotational inertia associated with renewable energy generators (REGs) pose challenges to grid stability. This paper proposes a novel mixed-integer linear programming (MILP) model that optimizes energy costs while enhancing system inertia in the presence of RE uncertainties. The model incorporates both REG uncertainties and inertia requirements into the planning process. To validate its effectiveness, RE uncertainty data from Mangaung Municipality, Free State Province, South Africa, is used for analysis, and the model is tested on the IEEE 6-bus test system. Simulation results demonstrate that the proposed approach improves system inertia from 5.875 s to 6.304 s while reducing energy costs from $1752.88/MWh to $1614.50/MWh in Case 3, where both RE uncertainties and system inertia are considered. These results show a clear advantage over Case 1 (cost minimization only) and Case 2 (inertia maximization only), highlighting the model’s ability to balance economic and stability objectives in renewable-integrated power grids.

关键词： Renewable energy Uncertainties System inertia Grid stability Mixed-integer linear programming

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata 700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata 700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： AL-QAOA Blockchain HQAOA MA-QAOA Quantum computing

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Fish Detection using Dual Branch Image Enhanced Pipeline with YOLOv8

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Procedia computer Science 2025年 258卷 1972-1979页

作者： Vivek Kumar Kumaran P. Dept. of Computer Science Engineering National Institute of Technology-Puducherry Karaikal-609609 UT of Puducherry

Fish species identification plays a crucial role in the aquaculture industry. Current methods such as using sensors, acoustic devices and density map regression, face multiple challenges related to costs, computational complexity and corrosion or malfunctioning of devices in underwater environment. To address these limitations we use YOLOv8(You Only Look Once) for the identification of fish species using visual perspective through the camera. The model is trained using a dataset of fish aquarium images to provide ground truth labels for training and evaluation purposes. Two species were observed in this research: Iridescent Shark Catfish (Pangasianodon hypophthalmus) and Goldfish (Carassius auratus). Our study achieved a notable breakthrough in identifying all the two species within a comprehensive dataset comprising 173 images. The top-performing model demonstrated exceptional accuracy on the test dataset with splitting ratios of 70-20-10 for training, validation, and testing, respectively. The custom model attained a mAP which is mean Average Precision, of 92% at an IoU threshold of 0.5, underscoring its impressive capabilities to detect across all classes. To remove the turbidity, a dual branch image enhancement pipeline is used with yolov8, which gives the best confidence score of 66% in detecting the fish species.

关键词： Object detection Yolov8s Underwater image enhancement turbidity visibility

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Machine Learning Models for Foreign Worker Prediction in Thailand: A Study of Linear Regression and Decision Tree

Machine Learning Models for Foreign Worker Prediction in Tha...

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2024 International Conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Jomsri, P. Prangchumpol, D. Eamthanakul, B. Poonsilp, K. Pudprommarat, C. Dept. of Information Technology Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Computer Science Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Applied Science Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand

ISBN: (纸本)9781510691773

This study proposed to predict the immigration patterns of foreign workers in Thailand using machine learning techniques. The primary focus is to analyze how factors such as the year, month, area, and legal immigration type (MOU) influence the number of foreign workers entering the country. MOU represents the Memorandum of Understanding, a legal framework for foreign migration. By utilizing two distinct machine learning models—Linear Regression and Decision Tree—this research seeks to identify the most effective method for forecasting immigration trends. This study analyzes foreign worker immigration in Thailand using data collected from 2560 to 2561 B.E. The data used in this study includes key variables that are indicative of immigration movements, providing a comprehensive view of the factors influencing foreign migration. The findings reveal that the Decision Tree model outperforms Linear Regression in terms of accuracy at 0.99. This analysis is crucial for policymakers and stakeholders as it provides insights into the patterns of foreign worker immigration, enabling better planning and management of foreign worker resources. Understanding these patterns is essential for developing effective immigration policies and ensuring that foreign worker market needs are met in a regulated and orderly manner. The comparative analysis demonstrates the importance of selecting appropriate machine learning models for accurate predictions in the domain of foreign immigration. © 2025 SPIE.

关键词： Linear regression

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