Information exchange and communication through the Internet are one of the most crucial aspects of today’s information technology world. The security of information transmitted online has grown to be a critical concern, particularly in the transfer of medical data. To overcome this, the data must be delivered securely without being altered or lost. This can be possibly done by combining the principles of cryptography and steganography. In the recent past, steganography is used with simpler methods like the least significant bit manipulation technique, in order to encode a lower-resolution image into a higher-resolution image. Here, we attempt to use deep neural networks to combine many two-dimensional colour images of the same resolution into a single cover image with the same resolution. In this technique, many secret images are concealed inside a single cover image using deep neural networks. The embedded cover image is then encrypted using a 3D chaotic map for diffusion and elliptic curve cryptography (ECC) for confusion to increase security.Supporting the fact that neural networks experience losses, the proposed system recovers up to 93% of the hidden image concealed in the original image. As the secret image features are identified and combined along with the cover image, the time complexity involved in the security process is minimized by 78% compared to securing the original data.

Rapid and accurate detection of plant diseases is crucial for agricultural productivity and food security. Traditional methods are labor-intensive and often unreliable. To overcome these limitations, this research introduces an innovative approach that integrates attention mechanisms into residual networks (ResNets) and utilizes Generative Adversarial Networks (GANs) for data augmentation. The method incorporates Attention-Augmented Residual Networks (AARN), which enhance feature extraction and classification by focusing on critical image regions. A Conditional GAN (cGAN) generates synthetic images of diseased and healthy plants, increasing dataset diversity. By combining AARN with Faster Region-Convolutional Neural Network (Faster-RCNN), detection capabilities are further enhanced. Training the AARN model on this augmented dataset improves generalization, achieving an impressive 98.78% accuracy in plant disease classification. The attention-augmented residuals boost the Faster-RCNN’s effectiveness by 23.84%, improving feature relevance and reducing overfitting. Comparative analysis shows that this method outperforms existing techniques in accuracy, precision, recall, and F1-score, offering a robust solution for plant disease detection. This integration of advanced deep learning techniques significantly improves automated plant disease identification, benefiting agricultural management practices.

Modern energy management systems must include load forecasting in order for utilities to plan and optimize electricity distribution, lower operating costs, and improve grid stability. With the addition of renewable energy sources and the advancement of smart grid technology, energy systems have become increasingly complex, making accurate forecasting increasingly challenging. Conventional techniques, including regression models and ARIMA, frequently perform less well because they are unable to capture the complex multivariate relationships and temporal dependencies present in energy data. Furthermore, these techniques are prone to errors in the presence of noisy data and have scalability issues when used on big, high-dimensional datasets. This paper presents a hybrid forecasting framework that combines artificial neural networks with Time Series Transformers and Fuzzy Logic Transform in order to overcome these drawbacks. The Transformer architecture excels in capturing long-term dependencies and interdependencies between features through its self-attention mechanism. Meanwhile, FLT + ANN effectively preprocesses noisy, irregular data and models short-term nonlinear patterns. The combination of these techniques creates a robust framework capable of handling complex energy datasets while maintaining high accuracy. Extensive tests on actual energy datasets show that the suggested hybrid model outperforms both conventional and stand-alone methods. With RMSE and MAE reductions of up to 15–20%, the model outperforms baseline models such as Random Forests, Decision Trees, and Linear Regression. These findings demonstrate how the suggested paradigm has the potential to transform load forecasting and enable more intelligent, effective energy systems.

The digital era has brought significant advancements in technology to such an extent that it has transformed people’s life. Assistive technology makes it possible to undergo tasks that are otherwise difficult for people who are differently-abled. For instance, voice-over capabilities and screen readers make it possible for the visually disabled to use various smartphone applications. Digital payment applications are one such application that significantly eases their daily financial transactions needs. There may, however, be several design issues while using such applications. The mobile applications designed should be designed equitably, keeping in mind the needs of people with disability. A usability study is conducted to assess a product by its actual testing done by a diverse audience. This study aims to evaluate Unified Payments Interface (UPI)-based digital payment solutions for the visually impaired. To identify the design considerations needed while designing digital payment applications, three UPI mobile applications (namely Google Pay, PhonePe, and Paytm) most used in India were selected and evaluated with fifteen visually impaired participants using standard accessibility guidelines. A broad objective of this study is to bring out the issues in accessibility features and suggest insights for re-designing these features to make mobile applications more accessible for visually impaired users. Usability tests were conducted on our volunteers to gather relevant data and a thorough analysis was thereby done. Our analysis reveals that while digital payment mobile applications provide accessibility features for visually impaired users, there may be significant design issues in areas such as screen reader compatibility, keyboard navigation, and clear labelling of buttons and controls. Furthermore, suggestions for improving the accessibility of digital payment mobile applications to serve the visually impaired community can be found based on the findings of the present research study. These suggestions can be useful while designing or updating digital payment mobile applications considering the needs of visually impaired people.

In the current circumstance, e-commerce through an online banking system plays a significant role. Customers may either buy goods from E-Commerce websites or use online banking to move money to other accounts. When a user participates in these types of behaviors, their sensitive information is sent to an untrustworthy network. As a consequence, when transmitting data from an internal browser to an external E-commerce web server using the cryptographic protocol SSL/TLS, the E-commerce web server ensures the security of the user’s data. The user should be pleased with the confidentiality, authentication, and authenticity properties of the SSL/TLS on both the user’s web browser and the remote E-commerce web server. E-Commerce web servers should choose the best SSL/TLS cipher suites for negotiating the user in order to attain such optimistic scenarios, as the cipher suite used in SSL/TLS plays an important role in securing E-Commerce web servers. The paper primarily focuses on analyzing the SSL/TLS cipher and elliptic curves. The paper also recommends the best elliptic curve cipher suites for E-Commerce and online banking servers, based on their power consumption, handshake execution time, and key exchange and signature verification time.