Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhance SER accuracy and generalization through innovative deep learning models. Despite its importance in various fields like human–computer interaction and mental health diagnosis, accurately identifying emotions from speech can be challenging due to differences in speakers, accents, and background noise. The work proposes two innovative deep learning models to improve SER accuracy: a CNN-LSTM model and an Attention-Enhanced CNN-LSTM model. These models were tested on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), collected between 2015 and 2018, which comprises 1440 audio files of male and female actors expressing eight emotions. Both models achieved impressive accuracy rates of over 96% in classifying emotions into eight categories. By comparing the CNN-LSTM and Attention-Enhanced CNN-LSTM models, this study offers comparative insights into modeling techniques, contributes to the development of more effective emotion recognition systems, and offers practical implications for real-time applications in healthcare and customer service.

For content-based image retrieval, the shape is one of the most important discriminatory elements. The form captures most of the perceptual information of the observed objects on images in many applications, while colour and texture can often be omitted without affecting the performance of the retrieval. Unfortunately, there may be significant changes in shape, such as deformation, scaling, changes in orientation noise, and partial concealment. Accurate shape description remains, therefore, a challenging technical issue. The study performs experimental analysis to identify the problem. The adoption of the MPEG-7 and KIMIA-99 standard has significant importance to simplify the image retrieval process. The Fourier Descriptors, Moment-Based Features, Hierarchical Centroids and Histogram of Oriented Gradients have been applied for extraction of images from datasets. The fusion of features has been done by Discriminant Correlation Analysis and Direct Concatenation of features it has been evident that by fusion of features we obtained approximately 90% accurate and better results.

Image registration is a manner of aligning two or more images of the same scene which achieves maximum accuracy. Analysis of geometric distortion, caused by the difference in viewing angle, sensor resolution and distance. The critical step in image registration is the collection of feature points and evaluate a spatial transformation mainly when outliers are present. In our proposed framework we have used the hybrid feature-based and area-based methods together. Before applying those methods, the Bilateral Filter is applied for the preprocessing of both the images, the reference image and the image that needs to be registered in order to make the images noise free. For feature detection, the ORB (Oriented FAST and Rotated BRIEF) is used which is basically a fusion of FAST key point detector and BRIEF descriptor with many modifications to enhance the performance. KNN (K-Nearest Neighbors) is used for matching similar points and for reducing the miss matches the efficient algorithm is used which is Random Sample Consensus (RANSAC). The template matching method is applied for the area-based matching.

Purpose: As a potentially disruptive technology, blockchain technology ensures that all the data cannot be merely tampered with once they are recorded on-chain. However, the fake source information may be input into the blockchain, which is mistaken for truthful data and results in a trust divide between the on-chain and the actual world. One missing perspective from previous studies is information manipulation at the source still exists under the blockchain mode. The authors’ goal was to analyze how blockchain technology affects the information deception of the agricultural product supply chain (APSC) under this premise. Also, the authors further analyzed some factors that influence the effectiveness of blockchain technology. Design/methodology/approach: The authors build an APSC game model consisting of a farmer and an agricultural product broker, which employs the principal–agent game model to explore the conditions for achieving the mutual trust equilibrium between the two parts. Then, through numerical simulation, the authors further analyze how the quality of on-chain information and the numbers of on-chain firms affect blockchain’s effect on deception in APSC and examine the circumstances in which blockchain technology is more suitable. Findings: The authors demonstrate that only by meeting the threshold of high-quality on-chain information and having a sufficient number of on-chain firms, can the blockchain-based supply chain initiate a better information ecosystem, which helps eradicate deception in the APSC. Originality/value: This paper provides valuable insights for participants in supply chains as well as is probably generalizable to other industrial products that require similar services in the early stage of blockchain.