Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages.

One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

The reliable and timely detection of cracks in concrete structures is essential for maintaining the safety, functionality, and longevity of civil infrastructure, including buildings, bridges, highways, and dams. Structural cracks can emerge due to multiple factors such as material fatigue, environmental stressors, seismic activity, and thermal expansion, necessitating accurate and efficient monitoring systems. Traditional inspection techniques, including manual visual inspection and non-destructive testing, are labour-intensive, prone to subjectivity, and often lack scalability. To address these limitations, the research presents CrackNet-VGG, a deep learning-based framework that leverages the VGG16 convolutional neural network architecture for automatic binary classification of surface cracks in concrete images. The proposed model leverages transfer learning by fine-tuning the VGG16 architecture on concrete surface datasets, utili sing its convolutional layers for robust feature extraction and its fully connected layers for final binary classification. The model is trained and evaluated on publicly available benchmark datasets, categorised into two classes: cracked and non-cracked surfaces. Experimental results demonstrate that CrackNet-VGG achieves a high classification accuracy of 96.07 %, with 95.57 % precision and 95.31 % recall, surpassing several baseline deep learning models in terms of accuracy. These results validate the applicability of CrackNet-VGG as an effective solution for automated concrete crack detection in real-world scenarios.

The modern tools used for video editing and compression make video tampering detection extremely difficult for digital forensic investigators, as it can often hide visual and temporal evidence or artifacts of tampering. In this work, we propose TamperNet, a hybrid spatio-temporal framework to detect various video manipulations that are common in both inter-frame and intra-frame domains such as frame duplication, frame deletion, cloning, splicing, and inpainting. The key design contribution is the spatio-temporal feature fusion by concatenation, a temporal representation module using LSTM, a spatio-temporal descriptor using ResNet-50, and a spatio-temporal anomaly scoring strategy based on temporal prediction error and autoencoder reconstruction error. Results on the Video Forgery Dataset (VFD) indicate an accuracy of 93.3%, a precision of 91.4%, a recall of 91.3%, an F1-score of 89.9%, and an ROC-AUC of around 0.75. These findings show that TamperNet works on the tested benchmark set, but further cross-dataset testing and compression-specific testing are required for assessing the potential for generalization to the real world, given the moderate ROC-AUC and small size of the tested benchmark set. Other future research will include validation on larger benchmark sets, making the framework more robust in various compression and acquisition scenarios, and the extension of the model to enable localization of tampering.