An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home

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A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System

In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract.publishedVersio

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

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SCBC: Internet of Things for smart city application monitoring systems with secured block chain technique and fuzzy algorithms

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%

SCBC: Internet of Things for smart city application monitoring systems with secured block chain technique and fuzzy algorithms

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method’s performance

A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System

In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract

Metaverse Framework: A Case Study on E-Learning Environment (ELEM)

Metaverse is a vast term that can contain every digital thing in the future. Therefore, life domains, such as learning and education, should have their systems redirected to adopt this topic to keep their availability and longevity. Many papers have discussed the metaverse, the applications to run on, and the historical progress to have the metaverse the way it is today. However, the framework of the metaverse itself is still unclear, and its components cannot be exactly specified. Although E-Learning systems are a need that has developed over the years along with technology, the structures of the available E-Learning systems based on the metaverse are either not well described or are adopted, in their best case, as just a 3D environment. In this paper, we examine some previous works to find out the special technologies that should be provided by the metaverse framework, then we discuss the framework of the metaverse if applied as an E-Learning environment framework. This will make it easy to develop future metaverse-based applications, as the proposed framework will make the virtual learning environments work smoothly on the metaverse. In addition, E-Learning will be a more interactive and pleasant process

A Serendipity-Oriented Personalized Trip Recommendation Model

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip

Enabling Two-Way Communication of Deaf Using Saudi Sign Language

Disabled people are facing many difficulties communicating with others and involving in society. Modern societies have dedicated significant efforts to promote the integration of disabled individuals into their societies and services. Currently, smart healthcare systems are used to facilitate disabled people. The objective of this paper is to enable two-way communication of deaf individuals with the rest of society, thus enabling their migration from marginal elements of society to mainstream contributing elements. In the proposed system, we developed three modules; the sign recognition module (SRM) that recognizes the signs of a deaf individual, the speech recognition and synthesis module (SRSM) that processes the speech of a non-deaf individual and converts it to text, and an Avatar module (AM) to generate and perform the corresponding sign of the non-deaf speech, which were integrated into the sign translation companion system called Saudi deaf companion system (SDCS) to facilitate the communication from the deaf to the hearing and vice versa. This paper also contributes to the literature by utilizing our self-developed database, the largest Saudi Sign Language (SSL) database—the King Saud University Saudi-SSL (KSU-SSL). The proposed SDCS system performs 293 Saudi signs that are recommended by the Saudi Association for Hearing Impairment (SAHI) from 10 domains (healthcare, common, alphabets, verbs, pronouns and adverbs, numbers, days, kings, family, and regions)