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Estimating parameters in solar cell models is crucial for simulating and designing photovoltaic systems. The single-diode, double-diode, and three-diode models represent these systems. Parameter estimation can be viewed as an optimization problem to minimize the difference between measured and estimated data. This study presents PV parameter estimation using the enhanced Sinh Cosh Optimizer (I_SCHO), incorporating trigonometric operators from the Sine Cosine Algorithm (SCA). This integration improves the algorithm’s ability to navigate complex search spaces, avoid local optima, and expedite convergence. Assessment criteria include runtime, convergence behaviour, minimum RMSE, and system reliability measured by SD. Results show that I_SCHO consistently delivers superior accuracy and reliability compared to other methods. Experiments were conducted on five solar cells: RTC France, Photowatt-PWP201, Kyocera KC200GT, Ultra 85-P, and STM6-40/36 module. The study also includes a comparative analysis using state-of-the-art algorithms, demonstrating I_SCHO’s efficiency through RMSE, Power Voltage (P-V) and Current Voltage (I-V) curves.

In modern air defense systems, safeguarding sensitive information is crucial to prevent unauthorized access and cyber-attacks. Here, we present an innovative image encryption approach, leveraging chaotic logistic maps and hyperchaotic Folded Towel Map sequence generation. The proposed image encryption is a multi-layered procedure intended to secure image transmission. It initiates with permutation, where a chaotic logistic map generates pseudo-random sequences to scramble pixel positions. Next, key mixing creates complexity, randomness, and nonlinearity using an invertible key matrix. Finally, the diffusion phase employs hyperchaotic maps to produce a new sequence XORed with the pixels through a bitwise operation, further encrypting the image. This three-stage process efficiently protects images from unauthorized access, ensuring secure transmission. The proposed method enhances security by leveraging non-linearity, sensitivity, and robust mixing, properties making it highly resistant to cryptographic attacks. The experimental results showed robust encryption performance as established by metrics such as an entropy value of 7.9991, a UACI of 33.21%, and an NPCR of 99.61%. The proposed encryption approach outperformed existing methods in securing image transmission and storage, offering a reliable solution for protecting air defense communication strategic data.

In the present research, we aim to confirm factors for measuring the perceived quality of Information Technology (IT) services within a higher education context. The perceived quality of IT services is complex and is often measured by multi-dimensional constructs, which requires designing a sufficiently valid scale. Drawing upon literature and expert input, this study has identified 5 dimensions of IT service quality. Using an empirical study, we prove that perception of IT Service Quality (ITSQ) has a dimensional structure and can be measured using a 44-item scale which has been satisfactorily validated. A Confirmatory Factor Analysis (CFA) is applied to confirm the relationship between the items and dimensions. The findings of this study present a scale for measuring ITSQ within higher education.

In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

The rapid progress of deepfake generation technologies poses a growing threat to the credibility of digital video content. Most existing solutions rely on reactive detection methods, which struggle to generalize against continuously evolving generative models. To address this challenge, this paper proposes a proactive and verifiable deepfake mitigation framework that secures video content at the point of creation. The proposed system integrates three complementary components: (i) a spatio-temporal attention model based on EfficientNetV2B0 and a two-layer Long Short-Term Memory (LSTM) network to identify perceptually and temporally salient regions, (ii) an attention-guided Generative Adversarial Network (GAN) to embed an imperceptible yet robust watermark, and (iii) a blockchain-based ledger to provide immutable file-level integrity verification via cryptographic hashing. Experimental results show that the attention model achieves 97.20% accuracy, confirming its ability to learn discriminative spatio-temporal representations. The watermarking scheme preserves high visual quality, achieving an average PSNR of 33.67 dB and SSIM of 0.9838. The proposed framework detects 100% of face-swapping attacks generated using DeepFaceLab 2.0 and demonstrates robustness against common video degradations, including recompression (90%), scaling (89%), and blurring (98%). Performance degradation under severe Gaussian noise (80%) highlights the practical limits of the watermark under non-structural pixel perturbations. An ablation study further confirms that both attention guidance and temporal modeling are critical to achieving high robustness. Overall, this work shifts deepfake defense from reactive detection to proactive, content-aware, and verifiable protection.

Speech emotion recognition (SER) is the process of predicting human emotions from audio signals using artificial intelligence (AI) techniques. SER technologies have a wide range of applications in areas such as psychology, medicine, education, and entertainment. Extracting relevant features from audio signals is a crucial task in the SER process to correctly identify emotions. Several studies on SER have employed short-time features such as Mel frequency cepstral coefficients (MFCCs), due to their efficiency in capturing the periodic nature of audio signals. However, these features are limited in their ability to correctly identify emotion representations. To solve this issue, this research combined MFCCs and time-domain features (MFCCT) to enhance the performance of SER systems. The proposed hybrid features were given to a convolutional neural network (CNN) to build the SER model. The hybrid MFCCT features together with CNN outperformed both MFCCs and time-domain (t-domain) features on the Emo-DB, SAVEE, and RAVDESS datasets by achieving an accuracy of 97%, 93%, and 92% respectively. Additionally, CNN achieved better performance compared to the machine learning (ML) classifiers that were recently used in SER. The proposed features have the potential to be widely utilized to several types of SER datasets for identifying emotions.

Water is one of the most critical and finite resources on our planet. As the demand for freshwater continues to grow, effectively managing and purifying existing water sources becomes increasingly important. This study introduces a Machine learning-based approach for enhancing water quality monitoring and anomaly detection in treatment plants using a modified Quality Index (QI). The proposed method integrates an encoder-decoder architecture with real-time anomaly detection and adaptive QI computation, providing a dynamic evaluation of water quality. In addition to developing this model, we present a comparative analysis with several existing machine learning models, demonstrating the effectiveness of our approach in detecting water quality anomalies. The revised QI is continuously updated using real-time sensor data, aiding decision-making in treatment operations. Experimental results show that the proposed model achieves superior performance, with an accuracy of 89.18%, precision of 85.54%, recall of 94.02%, Critical Success Index of 93.42%, Matthews Correlation Coefficient of 88.40%, delta-P of 94.37%, and Fowlkes–Mallow’s Index of 89.47%. These results highlight the model’s strong predictive capability and its practical utility in improving water treatment plant efficiency. By combining Machine learning with adaptive quality assessment, this study contributes to advancing intelligent monitoring solutions in water management.

This paper presents AquaFlowNet, a machine learning-based algorithm for real-time wastewater flow management. It addresses critical challenges related to operational efficiency, resource optimization, and environmental sustainability. Wastewater management systems require innovative methods for dynamic and efficient flow control to meet growing demands driven by urbanization, climate change, and increasingly stringent regulations. However, most existing methods rely on static or rule-based models, which lack the flexibility to handle fluctuating flow rates, variable environmental loads, and unforeseen disruptions. These limitations often lead to inefficiencies such as energy wastage, treatment delays, and overflow incidents, negatively impacting system performance and sustainability.AquaFlowNet leverages state-of-the-art machine learning algorithms to analyze real-time data from sensors, forecast flow variations, and optimize wastewater treatment processes. By integrating predictive analytics with intelligent control strategies, it enhances resource efficiency, prevents overflow events, and ensures regulatory compliance. Experimental evaluations demonstrate that AquaFlowNet outperforms conventional approaches in prediction accuracy and operational efficiency, reducing energy consumption, improving treatment effectiveness, and mitigating environmental impacts.The results highlight AquaFlowNet’s potential to revolutionize wastewater management systems, making them more resilient, adaptive, and beneficial for urban and industrial applications.

Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors. In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters. Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images. To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including temporal smoothing and trajectory encoding, to enhance feature learning. Tests on 1370 infant movement videos yielded impressive results: 94.7% sensitivity, 92.3% specificity, and an AUC-ROC of 0.968, outperforming ten state-of-the-art methods. Notably, it achieved a sensitivity of 96.3% within the critical 9–15 weeks range of fidgety movements, enabling timely interventions. Attention visualization highlights key areas such as the hips and shoulders, reinforcing clinical relevance. TransCP-Net demonstrates effectiveness across diverse clinical settings, serving as a viable, non-invasive tool for early cerebral palsy detection.