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1st IEEE International Conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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Optimizing Market Interventions a Data-Driven Approach to Price Stabilization

Optimizing Market Interventions a Data-Driven Approach to Pr...

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

作者： Venkatesh P Rupak S Keyas Ebenezar Robert Shakeel Ahamed E Department of Computer Science and Business Systems K.S.Rangasamy College of Technology Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research study proposes data-driven approaches to track and maintain prices of food products. It develops an all-inclusive database of market data based on real-time pricing information generated from reporting centers spread all over the country. Based on ARIMA forecasting models and news API, the system analyzes historical data, seasonal trends, and estimates of crop production to prepare proper predictions regarding future trends in pricing. It senses the price fluctuations and takes a call whether to uncheck the freed buffer stocks or wait for the market. These are projections that would enable active steps towards enabling a feasible and sustainable food supply chain through stabilization of prices, food safety, and optimum use of available resources.

关键词： Fluctuations Supply chains Pricing Predictive models Market research Real-time systems Food safety Food products Security Forecasting

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An Improved Deep Learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection 2nd

An Improved Deep Learning Framework Based on Multi-Scale Con...

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2nd International Conference on Data science and Network Engineering, ICDSNE 2024

作者： Idris, Idris Ya’u Ya’u, Badamasi Imam Ali, Usman Gulzar, Yonis Department of Computer Science School of Science and Technology Federal Polytechnic Bauchi Bauchi743001 Nigeria Department of Mathematical Sciences Faculty of Science Abubakar Tafawa Balewa University Bauchi740272 Nigeria Gombe760253 Nigeria Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa31982 Saudi Arabia

ISBN: (纸本)9789819783359

In addition to serving as a means of transportation, a highway connects the local economy. But as the pavement ages, several flaws including cracks, potholes, and deformation gradually show up on the surfaces of the road. Crack evaluation is typically done by hand using field surveys performed by humans. Nevertheless, these labor-intensive, time-consuming, manual survey methods are not reproducible or repeatable and place surveyors in dangerous situations. This proposes a novel deep-learning framework for road crack detection based on multi-scale convolutional architecture. To address the challenges of object occlusion, soft non-maximal suppression (NMS) is applied across crack proposals at various feature scales. The experimental results show that the introduced model attains an accuracy of 0.9989 and better detection performance across four distinct object proposal building block (OPBB) architectures. Thus, our model outperforms the benchmark models in every instance of OPBBs. In this way, the problem of large object scale variation in road crack images with different object occlusion challenges has been tackled, and the positive images are divided into a set of semantically significant regions, such as the road surface and cracks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Deep Learning for Cyber Security: Deephoax Detection 2

Deep Learning for Cyber Security: Deephoax Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Yakub, Sy. Yuliani Wowor, Rivo Juicer Agustriawan, David Irmawati Kristiyanti, Dinar Ajeng Winarno, P.M. Hassan, Rosilah Mohamad, Roshayu Razzaq, Randy Zahran Universitas Multimedia Nusantara Department of Informatics Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Uin Sultan Maulana Hasanuddin Department of Informatics Banten Indonesia

ISBN: (纸本)9798331506995

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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S3-GHOSTNET: skin disease detection via Sand Cat Swarm Optimization deep GHOSTNET with facial images

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Australian Journal of Electrical and Electronics Engineering 2025年

作者： Rathinam, Vinoth A, Ramathilagam K, Valarmathi S, Erana Veerappa Dinesh Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi India Department of Computer Science and Business Systems Ramco Institute of Technology Raiapalayam India

Skin is the largest organ in the human body and covers the entire body. Face skin diseases are a wide range of disorders that can affect the skin on the face. In this paper, a novel S3-GHOSTNET has been proposed to identify the face skin diseases. Initially, the input images are gathered from the Derm Net dataset and pre-processed using the Contrast stretching Adaptive Gaussian star Filter for enhancing the quality of the image. The enhanced images are fed into Modified Deep V3+ for segmenting the diseases affected region in the images. Then the segmented images are fed into deep learning-based Shuffle Net for extracting the features of the images. Finally, the required features are selected using Sand Cat Swap Optimization and using Ghost Net the Penta type of face skin diseases are classified. The proposed method achieves an accuracy rate of 99.34% in the normal classes and 98.94% in the abnormal classes. Additionally, the proposed method archives high-level Precision, Recall and F1 Scores of 98.05%, 97.73%, and 97.66% in the normal and abnormal classes. The proposed S3-GHOSTNET achieve the overall accuracy rate of 0.49%, 1.49%, 0.36% and 0.79% comparing the existing methods such as GLCN, Leaky RELU, CNN and Eff2Net, respectively. © 2025 Engineers Australia.

关键词： Adaptive filters

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A novel family of generalized stochastic neural network operators with illustrations of their various capabilities

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Ain Shams Engineering Journal 2025年第8期16卷

作者： Aljohani, Abeer Department of Computer Science and Informatics Applied College Taibah University Madinah42353 Saudi Arabia

This study introduces a novel family of generalized stochastic neural network (GSNN) operators with enhanced modeling and approximation capabilities. Point-wise and uniform approximation results are established, along with estimations of the rate of convergence. By utilizing vector functions and vector inputs, the proposed operators achieve improved convergence rates and effectiveness in handling complex data. Their applicability in optimization and modeling real-world phenomena is demonstrated through the approximation of the Rosenbrock function. A detailed comparative analysis confirms that the GSNN operators outperform several existing operators in terms of convergence speed, approximation order, and accuracy metrics, including the F1-score. Furthermore, the operators' modeling capabilities are validated using real-world cardiovascular disease data, where they exhibit superior predictive performance. The incorporation of stochastic processes enables the proposed operators to effectively capture and model the inherent uncertainties present in real-world scenarios. © 2025 The Author(s)

关键词： Linearization

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A Multi-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

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computers, Materials and Continua 2025年第3期83卷 5299-5321页

作者： Dillshad, Veena Khan, Muhammad Attique Nazir, Muhammad Ahmad, Jawad AlHammadi, Dina Abdulaziz Houda, Taha Cho, Hee-Chan Chang, Byoungchol Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar31952 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Center for Computational Social Science Hanyang University Seoul01000 Korea Republic of Department of Computer Science Hanynag University Seoul01000 Korea Republic of

Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer classification in innovative healthcare. The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset. The proposed architecture is based on two customized, innovative Convolutional neural network (CNN) models based on small depth and filter sizes. In the first model, four residual blocks are added in a squeezed fashion with a small filter size. In the second model, five residual blocks are added with smaller depth and more useful weight information of the lesion region. To make models more useful, we selected the hyperparameters through Bayesian Optimization, in which the learning rate is selected. After training the proposed models, deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique. The final features are passed on to the classifiers, and classification results are obtained. Also, the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset. The experimental process of the proposed architecture is performed on two dermoscopic datasets, HAM10000 and ISIC2019. We obtained an improved accuracy of 90.8% and 99.3% on these datasets, respectively. Also, the proposed architecture returned 91.6% for the cancer localization. In conclusion, the proposed architecture accuracy is compared with

关键词： Patient treatment

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Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative systems

arXiv

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arXiv 2025年

作者： de Berardinis, Jacopo Porcaro, Lorenzo Meroño-Peñuela, Albert Cangelosi, Angelo Buckley, Tess Department of Computer Science University of Liverpool United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics King’s College London United Kingdom Department of Computer Science University of Manchester United Kingdom Association of AI Ethicists

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission – a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://***/raim-framework/. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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