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

作者： Hossam M. J. Mustafa Nedal Nwasra Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel CSSA (Color Space Search Algorithm), introducing a metaheuristic optimization algorithm that addresses critical limitations in existing optimization methods. While current algorithms demonstrate suboptimal performance in multimodal landscapes, revealing premature convergence rates, CSSA introduces a novel approach by leveraging color theory principles in the optimization domain. The proposed algorithm combines complementary color transformations for global exploration with triadic color relationships for local exploitation, which aims to improve the exploration-exploitation balance compared to conventional methods. The algorithm's performance is evaluated across three categories of benchmark functions: unimodal functions, multimodal functions, and fixed-dimensional multimodal functions. Comprehensive experiments compared CSSA against classical (PSO, GA) and modern (MPA, EO, ALO, SSA, DA) optimization algorithms, with performance metrics including best fitness, mean fitness, solution consistency, and convergence rate analysis. Statistical analyses through the Friedman test (ranking 4.22) confirm the algorithm's robust performance, particularly in handling complex optimization landscapes. These results place CSSA as a promising optimization algorithm for complex optimization problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Optimization methods Color Evolutionary computation Benchmark testing Optimization Convergence

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Separate Source Channel Coding Is Still What You Need:An LLM-Based Rethinking

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ZTE Communications 2025年第1期23卷 30-44页

作者： REN Tianqi LI Rongpeng ZHAO Mingmin CHEN Xianfu LIU Guangyi YANG Yang ZHAO Zhifeng ZHANG Honggang College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Shenzhen CyberAray Network Technology Co. Ltd.Shenzhen 518000China China Mobile Research Institute Beijing 100053China The Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511453China Zhejiang Lab Hangzhou 311121China Faculty of Data Science City University of MacaoMacao 999078China

Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ***,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for *** demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over *** proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point *** comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication *** other words,separate source channel coding is still what we need.

关键词： separate source channel coding(SSCC) joint source channel coding(JSCC) end-to-end communication system Large Language Model(LLM) lossless text compression Error Correction Code Transformer(ECCT)

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN Computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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LossControl: Defending Membership Inference Attacks by Controlling the Loss

LossControl: Defending Membership Inference Attacks by Contr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Yang Hongwei Yang Renhao Lu Hui He Weizhe Zhang Haoyu He Rahul Yadav Faculty of Computing Harbin Institute of Technology Harbin China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Machine learning models are vulnerable to membership inference attacks (MIAs), where adversaries attempt to predict whether specific samples are part of the model’s training set. Previous studies have demonstrated a strong correlation between the distinguishability of training and testing loss distributions and the model’s susceptibility to MIAs. Motivated by existing results, we propose a novel training framework called LossControl, which focuses on manipulating loss to mitigate privacy leaks. In LossControl, we first utilize Soft-label Training to replace the general learning process, which facilitates model training while improving generalization. Next, we monitor overfitting samples during the training process and prevent further loss reduction by applying our designed Loss Ascent to these samples without sacrificing model performance. Through extensive evaluations across four diverse datasets (including images, medical data, and transaction records), our method consistently outperforms defense mechanisms against state-of-the-art attacks and achieves optimal model performance in most experiments, demonstrating LossControl’s superior resilience against MIAs and its ability to strike an unparalleled balance between privacy and utility.

关键词： Training Privacy Process control Machine learning Predictive models Signal processing Speech processing Testing Resilience Overfitting

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Enhancing Speech Emotion Recognition With Conditional Emotion Feature Diffusion and Progressive Interleaved Learning Strategy

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1787-1800页

作者： Yang Liu Xin Chen Zhichao Peng Yongwei Li Xingfeng Li Peng Song Masashi Unoki Zhen Zhao School of Information Science and Technology Qingdao University of Science and Technology Qingdao China School of Information Hunan University of Humanities Science and Technology Loudi China CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Faculty of Data Science City University of Macau Macau China School of Computer and Control Engineering Yantai University Yantai China School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, CASIA, and EMODB corpora demonstrates significant performance improvements in SER.

关键词： Feature extraction Emotion recognition Training Three-dimensional displays Diffusion models Speech enhancement Electronic mail Speech recognition Semantics Face recognition

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A Parallel Color Image Encryption Algorithm Based on a 2D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Ho-Ching Iu, Herbert Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong The School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China School of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computer Engineering Faculty of Engineering Karamanoglu Mehmetbey University Karaman70200 Turkey Department of Artificial Intelligence and Data Engineering Engineering Faculty Ankara University Ankara06830 Turkey Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This paper aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2D Logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multi-timescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a two-dimensional framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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A Hybrid Deep Learning-based Person Re-identification Across Multi-Cameras 10

A Hybrid Deep Learning-based Person Re-identification Across...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Aonon, Manaschai Charoenkwan, Phasit Singkhamfu, Phudinun Kaewkamol, Porntida Chiang Mai University Data Science Consortium Faculty of Engineering Chiang Mai Thailand Chiang Mai University Modern Management and Information Technology College of Arts Media and Technology Chiang Mai Thailand Chiang Mai University Software Engineering College of Arts Media and Technology Chiang Mai Thailand

ISBN: (纸本)9798331543273

Surveillance systems play an essential role in enhancing security and ensuring safety within society. Public spaces and critical areas are commonly monitored through multi-camera networks for tracking individuals in real-time to enable quick responses to potential security threats. However, variations in lighting, camera angles, and occlusions pose significant challenges to the process of recognizing individuals across multiple camera views. This research proposes a Person Re-Identification (Re-ID) with deep learning and feature extraction method to improve the accuracy of individual matching and identification despite these challenges. This research encompasses multiple neural network topologies, such as ResNet50, ResNet101, and OSNet architectures with traditional computer vision methodologies such as Scale-Invariant Feature Transform (SIFT) to examine the body part weighting coefficients and feature selection mechanisms. The results demonstrated that integrating dimensionality reduction via principal component analysis (PCA) coupled with semantic segmentation techniques yields statistically significant improvements in re-identification accuracy. The ResNet101 architecture emerges as the superior methodology, achieving performance with a Rank-1 accuracy of 0.88 and mean Average Precision (mAP) of 0.94 on the test dataset. © 2025 IEEE.

关键词： Semantic Segmentation

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Hybrid Transfer Learning and Deep Learning for Cloud Intrusion Detection Using Bio-Inspired Optimization

Hybrid Transfer Learning and Deep Learning for Cloud Intrusi...

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

作者： Salam Al-E'mari Yousef Sanjalawe Waleed Awwad Hamzah Alqudah Mohammed Aladaileh Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba Amman Jordan Department of Computer Science Faculty of Information Technology University of Petra (UoP) Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper proposes a novel hybrid model for cloud intrusion detection, integrating transfer learning with bio-inspired optimization techniques to enhance detection accuracy and efficiency. Cloud environments present unique challenges due to their dynamic and shared-resource nature, making traditional Intrusion Detection Systems (IDS) inadequate. The proposed model leverages Convolutional Neural Networks (CNNs) for feature extraction, utilizing pre-trained models through transfer learning to reduce training time while maintaining high detection performance. In addition, bio-inspired optimization algorithms, specifically Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO), are employed to fine-tune the CNN's hyperparameters, such as learning rate and batch size, ensuring optimal performance. The hybrid approach addresses the limitations of manual tuning and improves the model's ability to generalize across different cloud environments. Using the CICIDS 2017 dataset, the proposed system demonstrates superior performance in detecting both known and unknown threats, including zero-day attacks. The experimental results show significant improvements in accuracy of 99.3%, precision of 98.7%, recall of 98.5%, and an F1-score of 98.6% compared to state-of-the-art models.

关键词： Training Accuracy Biological system modeling Heuristic algorithms Transfer learning Intrusion detection Convolutional neural networks Particle swarm optimization Optimization Tuning

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Design an Ensemble Pretrained Deep Learning Model for Classification of Melanoma Skin Cancer Images

Design an Ensemble Pretrained Deep Learning Model for Classi...

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

作者： Dheeb Albashish Nedaa Almansour Azizi Abdullah Hossam M. J. Mustafa Manar Rizik AlSayyed Oraib Alrashdan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Center For Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Pathology Department University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The recent global outbreak of melanoma skin cancer has prompted increased attention toward computer-aided diagnosis systems (CAD). The primary application of CAD in melanoma is to discriminate between benign and malignant skin medical images. Additionally, the survival rate improves with early cancer detection. This study presents a CAD diagnosis method for melanoma skin diagnosis, which utilizes deep learning and medical image processing. To elaborate, the proposed model comprises two stages: Firstly, fine-tune three powerful pretrained models, namely VGG16, NASNetMobile, and ResNet50V2, to address the current task. Secondly, we leverage their decisions through ensemble learning and hard voting techniques. The evaluation of the proposed ensemble model on a skin cancer dataset yielded a notable accuracy of 91.5% on a test portion of the dataset. This method reveals the potential for diagnosing melanoma while also illustrating the efficacy of the ensemble model in augmenting diagnostic performance.

关键词： Deep learning Solid modeling Design automation Computational modeling Melanoma Skin Computer aided diagnosis Ensemble learning Medical diagnostic imaging Computational intelligence

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation Systems with Neural Collaborative Filtering and Sentiment Analysis

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SN Computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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