2690-6791ISSN (Online)
2769-786XISSN (Print)
The security with the protection of IoT is to stay a consequential test, for the most part, because of the huge scale and dispersed nature of IoT systems. A cloud server brings wide pertinence of IoT in numerous businesses just as Government parts. Be that as it may, the security concerns, for example, verification and information protection of these gadgets assume a key job in fruitful coordination of two innovations. To build the security here, a quantum hash work system and hybrid cuckoo search-Artificial Bee Colony algorithm is displayed. A quantum hash work has been presented as an amazing system for secure correspondence of IoT and cloud because of its irregular disordered robust execution, greater affectability for introductory authority dimension, steadiness, and the exceptionally huge crucial area is hypothetically sufficiently able to oppose different known assaults. Cloud servers utilizing CS-ABC to upgrading the safe calculations through a quantum channel inside the cloud framework. Execution examinations and recreation outcomes demonstrate our presented methods are portrayed and also have greater safety, proficiency with strength opposed to a few surely understood assaults which choose them as a great contender for verifying cloud and IoT applications.
Read MoreDoi: https://doi.org/10.54216/JISIoT.010201
Vol. 1 Issue. 2 PP. 61-71, (2020)
Arabic is one of the phonetically complex languages, and the creation of accurate speech recognition system is a challengeable task. Phonetic dictionary is essential component in automatic speech recognition system (ASR). The pronunciation variations in Arabic are tangible and are investigated widely using data driven approach or knowledge based approach. The phonological rules are used to get the pronunciation of each word accurately to reduce the mismatch between the actual phoneme representation of the spoken words and ASR dictionary. Several studies in Arabic ASR system are conducted using different number of phonological rules. In this paper we focus on those rule that handle within-word pronunciation variation and cross-word pronunciation variation. The experimental results indicate that handling within-word pronunciation variation using phonological rule doesn’t enhance the recognition performance, but using these rules to handle cross-word variation provide a good performance.
Read MoreDoi: https://doi.org/10.54216/JISIoT.010202
Vol. 1 Issue. 2 PP. 72-79, (2020)
Wireless sensor network (WSN) comprises a set of sensor nodes, mainly used for data collection and tracking process. The imaging sensors in WSN captures the images from the target environment, which needs to be securely transmitted to the base station (BS). Since data transmission in WSN takes place through wireless links, security is a major challenging issue involved in the design of WSN. Image encryption is a commonly available solution to securely transmit the images to destination without comprising security. Therefore, this study designs a novel Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption (CBO-OMKIE) technique for WSN. The goal of the CBO-OMKIE technique is to securely encrypt the images in WSN. The proposed CBO-OMKIE technique involves the design of multi-key based image encryption technique to accomplish security in WSN. In addition, the CBO algorithm is applied to determine the optimal keys involved in the encryption process and it helps for improving the security level to a maximum extent. The performance validation of the CBO-OMKIE technique takes place using benchmark test images and the outcomes were examined under several aspects. The simulation outcome pointed out the enhanced security analysis of the CBO-OMKIE technique over the other techniques.
Read MoreDoi: https://doi.org/10.54216/JISIoT.010203
Vol. 1 Issue. 2 PP. 80-92, (2020)
This study provides a means for institutions and administrations to develop plans while taking into consideration the strategic linkages. Making strategic decisions on their programming may benefit institutions and governments when relevant material is examined and talks with higher education specialists are held (HE). To handle disagreement and different criteria, multi-criteria decision-making (MCDM) models are utilized. The most effective solution was evaluated using the new multi-criteria technique known as MABAC (Multi-Attributive Border Approximation area Comparison). Following the computation of the criterion weights, the MABAC is used to rank the options. The recommended approach may be used by institutions as well as central planners (usually the government) in higher education policy.
Read MoreDoi: https://doi.org/10.54216/JISIoT.010204
Vol. 1 Issue. 2 PP. 93-101, (2020)
This paper proposes a deep learning-based intelligent waste management system that can accurately classify waste types and optimize waste disposal processes. The proposed system utilizes a convolutional model to concisely identify the waste type from images captured by a camera system. Our system uses intelligent data augmentation to perform large datasets of waste item images and achieves a high classification accuracy rate. The waste types are classified into several categories, including glass, cardboard, metal, plastic, paper, and trash. Experimental results show that our system achieves high accuracy rates in waste classification and improves waste disposal efficiency compared to traditional waste management systems. Our system has the potential to significantly reduce the negative impact of waste on the environment and to promote sustainable waste management practices.
Read MoreDoi: https://doi.org/10.54216/JISIoT.010205
Vol. 1 Issue. 2 PP. 102-108, (2020)