One of the most significant ways to improve energy reliability and lessen reliance on fossil fuels is to combine renewable energy sources with energy storage systems. Using wind, solar, and battery storage as case studies, the article examines hybrid renewable energy system (HRES) size, optimization, techno-economic potential, and reliability in extensive detail. In order to minimize expenses and emissions while satisfying energy demands, the sizing process consists of evaluating the appropriate capacity of each component. In order to tackle the growing demand with a low carbon footprint, this study investigates the prospect of leveraging hybrid renewable energy (HRE) storage. System demands, budget, and performance indicators are some of the most critical considerations when selecting an energy storage system (ESS) for a renewable energy system. Whether or not the storage option is appropriate for HRE systems depends on the setup requirements. The selection of an ESS technology necessitates taking into account aspects like energy and power requirements, efficiency, cost-effectiveness, versatility, and reliability. The investigation also considers factors such as electricity long-term viability, dependability, technical and financial feasibility, and ecological sustainability when evaluating hybrid renewable energy power-producing technologies. Improving dependability and smoothing down power output are two major benefits of ESS integration. The findings show that integrating HRES with ESS can lead to more sustainable energy systems, providing a long-term, reliable, and cost-effective solution. Findings emphasize the need for further study of optimization methods, meta-heuristic algorithm strategies, system components, design constraints, and desired techniques.

The AES (Advance Encryption Standard) has made the development of new block ciphers unnecessary; it is now the de facto standard for most uses of block ciphers. However, the AES is still not well-suited for very limited contexts like RFID (Radio-Frequency Identification) tags and WSN(Wireless Sensor Networks), despite recent implementation advancements. In this study, we present SHC (Simple Hybrid Cipher), a new block cipher that uses a 64-bit block length and a 128-bit key length. It offers a hardware implementation that efficiently uses limited resources, making it ideal for use as a sensor in a WSN or an RFID tag. The core function of SHC depends on S-Box-based, composite field arithmetic technology, as it consumes relatively low cost on hardware implementation while still providing sufficient security as a solid encryption algorithm. The hardware implementation of SHC-64 requires 949 LUTs; it generates a maximum operating frequency of 515.995 MHz on the Xilinx-powered Artix-7 Field Programmable Gate Array (FPGA) development board. At the same time, the National Institute of Standards and Technology (NIST) recommended standard algorithm AES consumes 3645 LUTs and generates a maximum operating frequency of 277.369 MHz. The SHC-64 cipher also shows resistance against known cryptanalytics attacks.

Small file processing in Hadoop is one of the challenging task. The performance of the Hadoop is quite good when dealing with large files because they require lesser metadata and consume less memory. But while dealing with enormous amount of small files, metadata grows linearly and Name Node memory gets overloaded hence overall performance of the Hadoop degrades. This paper presents a dual merge technique HB-EHA (Hash Based-Extended Hadoop Archive), that will resolve the small file issue of Hadoop and provide an excellent solution for massive small files that are generated in the health care management applications. The proposed technique merges the small files using two-level compaction, therefore, the size of metadata at the name node gets reduced and less memory will be used. The indexing will be carried out over the archives and files can be accessed after merging in real-time. Index files in the proposed approach can read partially that improves the name node memory usage and also offers the file appending capability in the existing archive. The proposed technique first creates Hadoop archive from the small files and then uses two special hash functions i.e. SSHF (Scalable-Splittable Hash Function) and HT-MMPHF (Hollow Trie Monotone Minimal Perfect Hash Function), SSHF is used to dynamically distribute the archives meta-data to the associated slave index files, and these slave index files will be further written to the final index files, the order of the meta-data in final index file will be preserved by the HT-MMPHF. The evaluation outcome exhibit that the proposed technique is 13% & 17% faster than HDFS with caching enabled and disabled respectively, and 38% & 47% faster than the HAR with caching and without caching, respectively. While comparing with the map file, the proposed technique is 28 & 35 times faster with caching and without caching, respectively. HB-EHA is a maximum of 40% & 28% faster than the HBAF with and without caching, respectively.

Plant diseases are a major problem for farmers around the world, reducing crop yields. The absence of expertise makes plant disease detection difficult and complicated. Plant disease detection is made easier by deep learning algorithms; however, they are computationally demanding and need huge training datasets. This research work proposes a novel Conv-7 DCNN model with modified ParNet attention layer to classify plant leaves into distinct categories with improved accuracy. Because of its architecture, the proposed network can identify leaf diseases with more accuracy by extracting the wider range of features from the images. The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories. The CNN model is trained using publicly accessible Kaggle dataset, utilising image augmentation techniques. It is evident from the simulation results that the proposed model outperforms several pre-trained, and other trending deep learning models. Proposed model achieves 99.18% classification accuracy with an average precision of 99.17% and area under the curve (AUC) of 1, making this model highly effective in leaf diseases detection. Additionally, Conv-7 DCNN achieved high FPS of 112.49, low inference time of 18.34 s, and low GFLOPS of 13.98, making it suitable for real-time applications in smart agriculture systems.

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.