Rashid Jahangir^1,*, Nazik Alturki², Muhammad Zubair Khan¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080593 - 23 July 2026

Abstract Deepfake audio created with sophisticated speech synthesis and voice cloning technologies is a threat to the credibility of digital communication. Its realism has raised serious concerns in different applications such as digital forensics, cybersecurity, media authentication and voice-based security systems. However, deepfake audio detection still remains difficult. Synthetic speech tends to have subtle artifacts that can mimic the natural vocal pattern very closely. Variations in speakers, recording conditions and background noise make the task more complex. In addition, dataset imbalance and low diversity in training samples could lead to low robustness in the model. To… More >

Rashid Jahangir^1,*, Muhammad Asif Nauman², Oumaima Saidani³, Faisal Ramzan²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.080025 - 23 July 2026

Abstract Speech Emotion Recognition (SER) is a critical component of affective computing with broad applications in human–computer interaction, mental health monitoring, and intelligent multimedia systems. However, SER remains challenging due to the emotional ambiguity, lack of labeled data, class imbalance, and speaker variability. This study presents an effective SER framework that integrates contrastive representation learning, optimized spectrogram-based data augmentation, and selective synthetic data generation by using TimeGAN to enhance emotion classification performance. Contrastive learning enables the model to better discriminate acoustically similar emotions while Optuna automatically tunes augmentation strategies such as noise injection, time shifting, and More >

Huaixuan Yan, Yan Gong^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080178 - 15 June 2026

Abstract To correctly diagnose faults in farm machinery, we need to know a lot about the field and have experience with maintenance. However, most of this important information is stored in old, unstructured documents like technical manuals and expert logs. These documents don’t have a standard way to be represented digitally, which makes it very hard to build automated diagnosis systems. There are three main technical problems with getting structured knowledge out of this kind of text: noise from optical character recognition (OCR) during digitization, the extreme lack of labeled samples in specialized fields (low-resource constraints),… More >

Fengqi Hao^1,2,3, Yawen Hou^2,3, Conghui Gao^2,3, Jinqiang Bai^2,3, Gang Liu⁴, Hoiio Kong^1,*, Xiangjun Dong^1,2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077252 - 09 April 2026

Abstract Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware… More >

Sudhir Kumar Pandey¹, Jian-Xun Mi^1,*, Zahid Ullah², Mona Jamjoom³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074811 - 09 April 2026

Abstract Adversarial examples in object detection frequently fail to transfer between different models because attacks overfit to the source model’s architecture and feature space. We propose AugTrans, a framework that addresses this limitation through input-space regularization. Our key innovation is a multi-stage augmentation pipeline that incorporates object-level semantic awareness into transformation design. The pipeline comprises three novel components: dynamic object-centric rotation with adaptive scheduling, multi-box aware resizing based on ground-truth annotations, and composite noise injection. These transformations are integrated within the Expectation over Transformation (EOT) framework. By optimizing perturbations to remain effective across semantically meaningful transformations, our… More >

Hatim Madkhali^1,2,*, Abdullah Sheneamer², Linh Nguyen³, Gnana Bharathy¹, Ritu Chauhan⁴, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074444 - 09 April 2026

Abstract Consumer electronics, with 62 million tons of electronic waste (e-waste) generated in 2022 and e-waste expected to grow to 82 million tons annually by 2030, pose critical challenges when it comes to national infrastructure and circular economy policies. This paper compares forecasting approaches using sparse panel data for 32 European countries (2005–2018, Eurostat/Waste Electrical and Electronic Equipment (WEEE) Directive), focusing on leakage-safe prospective validation to guarantee true predictive performance. We make one-step-ahead predictions with conservative features (primarily lagged values) to account for temporal autocorrelation but with reduced multicollinearity (Variance Inflation Factor (VIF) ≈ 1.0). Cross-paradigm comparisons… More >

Wasfieh Nazzal¹, Ezequiel López-Rubio^1,2,3, Miguel A. Molina-Cabello^1,2,3, Karl Thurnhofer-Hemsi^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077561 - 12 March 2026

Abstract Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning. Recent advances in foundation models, such as the medical-focused Segment Anything Model (MedSAM), have demonstrated strong performance but face challenges in many medical applications due to anatomical complexity and a limited domain-specific prompt. This work introduces a methodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts, rather than relying on single inputs. The proposed approach randomly samples sets of spatially distributed point prompts based on image features, enabling MedSAM to better capture fine-grained anatomical… More >

Chih-Yung Huang^*, Hong-Ru Shi, Min-Yan Xie

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075398 - 12 March 2026

Abstract Quality control plays a critical role in modern manufacturing. With the rapid development of electric vehicles, 5G communications, and the semiconductor industry, high-speed and high-precision detection of surface defects on silicon carbide (SiC) wafers has become essential. This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage. The complex machining textures on wafer surfaces hinder conventional machine vision models, often leading to misjudgment. To address this, deep learning algorithms were applied for defect classification. Because defects are rare and imbalanced across categories, data augmentation was performed… More >

Soo-Yeon Jeong¹, Ho-Yeon Jeong², Sun-Young Ihm^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074016 - 12 March 2026

Abstract Sign language is a primary mode of communication for individuals with hearing impairments, conveying meaning through hand shapes and hand movements. Contrary to spoken or written languages, sign language relies on the recognition and interpretation of hand gestures captured in video data. However, sign language datasets remain relatively limited compared to those of other languages, which hinders the training and performance of deep learning models. Additionally, the distinct word order of sign language, unlike that of spoken language, requires context-aware and natural sentence generation. To address these challenges, this study applies data augmentation techniques to… More >

Léa Bollen^1,*, Stéphane Rysselinck², Jean-Philippe Salmin³, Gilles Dosin⁴

Canadian Journal of Urology, Vol.33, No.1, pp. 221-225, 2026, DOI:10.32604/cju.2025.067192 - 28 February 2026

Abstract Background: Penile augmentation through injectable substances is becoming increasingly common. A growing number of aesthetic clinics are developing penile enlargement procedures using various injectable materials. Although these procedures are now performed in more controlled and medically supervised environments, their long-term outcomes remain poorly understood. The promotion of such medical treatments contributes to an increasing interest among adult males in self-injection as a method to alleviate psychological distress associated with penile size concerns. At the same time, access to injectable substances through unofficial or unregulated sources has become increasingly easy. Tor our knowledge, we report the… More >