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Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.

The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.

Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months. A former study divided the disease into four main stages. These stages have proven helpful for doctors to analyze the liver’s condition. The traditional way to check the staging is the biopsy, which is a painful and time-consuming process. This article aims to provide an effective and efficient approach to predict hepatitis C staging. For this purpose, the proposed technique uses a fine Gaussian SVM learning algorithm, providing 97.9% accurate results.

Smart healthcare applications depend on data from wearable sensors (WSs) mounted on a patient’s body for frequent monitoring information. Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures. The collection of WS data and integration of that data for diagnostic purposes is a difficult task. This paper proposes an Errorless Data Fusion (EDF) approach to increase posture recognition accuracy. The research is based on a case study in a health organization. With the rise in smart healthcare systems, WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness. As a result, it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency. Sensor breakdowns, the constant time factor, aggregation, and analysis results all cause errors, resulting in rejected or incorrect suggestions. This paper resolves this problem by using EDF, which is related to patient situational discovery through healthcare surveillance systems. Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures. This technology improves position detection accuracy, analysis duration, and error rate, regardless of user movements. Wearable devices play a critical role in the management and treatment of patients. They can ensure that patients are provided with a unique treatment for their medical needs. This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis. At first, the patients’ walking patterns are tracked at various time intervals. The characteristics are then evaluated in relation to the stored data using a random forest classifier.

The fifth generation (5G) system is the forthcoming generation of the mobile communication system. It has numerous additional features and offers an extensively high data rate, more capacity, and low latency. However, these features and applications have many problems and issues in terms of security, which has become a great challenge in the telecommunication industry. This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity, which randomly changes and does not use the permanent identity between the user equipment and home network. Through this mechanism, the user identity privacy would be secured and hidden. Moreover, it improves the synchronization between mobile users and home networks. Additionally, its compliance with the Authentication and Key Agreement (AKA) structure was adopted in the previous generations. It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector's message size. Moreover, the addition of any hardware to the AKA carries minor adjustments on the network parties. In this paper, the ProVerif is used to verify the proposed scheme.

Background A primary threat to food security stems from the expanding global population and climate change, which have increased the frequency of droughts. Owing to shifting climatic conditions, abiotic stresses such as severe drought are intensifying, reducing wheat productivity. This study aimed to evaluate the response of elite drought-tolerant wheat genotypes to water deficit stress by analysing agronomic and physio-biochemical traits, with the goal of identifying promising genotypes for breeding. Methods Twenty wheat genotypes sourced from various national and international drought-tolerant nurseries, including a benchmark variety, were tested under water deficit and well-watered conditions over two consecutive years. The data collected included agronomic traits such as plant height (PH), days to heading (DH), days to anthesis (DA), days to physiological maturity (DPM), canopy temperature, SPAD values at different growth stages, intercepted photosynthetically active radiation above the canopy (IPARAC) and on the ground (IPAR OG), yield stability index (YSI), stress tolerance index (STI), stress index (SI), leaf area index (LAI), spike length (SL), grains per spike (GPS), 1000-grain weight (TSW), grain yield (GY; t/ha), and biomass yield (BY; t/ha). Results To streamline the study, two years of aggregated data were analysed for each parameter. Drought tolerance was assessed based on grain yield, and multitrait genotype‒ideotype distance (MGIDI) indices were employed to select drought-tolerant wheat genotypes. Significant differences were observed among the wheat genotypes across all measured parameters under both conditions. Under normal conditions, correlation analysis revealed that grain yield (GY) and biomass yield (BY) had the strongest positive relationship ( r  = 0.75**), followed by TSW, LAI, GPS, SL, PH, DPM, and DA. In contrast, under water deficit stress, BY exhibited a notable correlation with plant height (PH) ( r  = 0.42). Under both irrigated and water deficit stress situations, GY had positive and substantial correlations with PH, DA, DPM, GPS, SL, the STI, and the YSI. Two of the ten main components (PCs) accounted for 52.3% and 50.4% of the overall variation under water deficit and well-watered conditions, respectively. Additionally, the genotypes were separated into three clusters via a cluster heatmap, and the most tolerant genotypes (E38, E40, E41, E35, and E33) were found to be in cluster 3, which revealed their genetic relatedness. Genotypes E9 and E29 were found to be sensitive to water deficit, whereas genotypes E40, E38, and E35 were drought tolerant, according to tolerance indices. Conclusion Plant breeders may find the MGIDI useful for selecting genotypes on the basis of a variety of characteristics because it is a straightforward and robust selection method. Among the 20 wheat genotypes, the most stable and productive were E38, E30, E35, E40, and E34, according to an analysis of MGIDI for diverse settings. This was likely caused by the high MPS (mean performance and stability) of specific traits under different situations. The features that have been identified can be used as genitors in hybridization procedures to create wheat breeding materials that are resistant to drought. The genotypes and features that were found to be drought tolerant could be used to create new genotypes that are resistant to drought stress.

Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily identifies the changes made in the data due to proper structure. The developed model supports privacy-preserving public auditing to provide a secure cloud storage system. The data owners upload the data in the cloud and edit the data using the private key. An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches. The data files requested by the data owner are audit by a third-party auditor and the multi-owner authentication method is applied during the modification process to authenticate the user. The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.

Flying ad hoc networks (FANETs) present a challenging environment due to the dynamic and highly mobile nature of the network. Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission. Several different techniques are adopted to address the issues arising in FANETs, from game theory to clustering to channel estimation and other statistical schemes. These approaches mostly employ traditional concepts for problem solutions. One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes. Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes. One such species that resembles the dynamicity of FANETs are Bats. In this paper, the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBAT-COOP (Improved BAT Algorithm using Cooperation technique). We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements. Moreover, we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs. This paper employs cooperation for an optimal route selection and reflects on distance, Signal to Noise Ratio (SNR), and link conditions to an efficient level to deal with FANET’s routing. By way of simulations, the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio, end-to-end delay, and transmission loss by 81%, 21%, and 82% respectively. Furthermore, the average link duration is improved by 25% compared to the BAT-FANET protocol.

With the evolution of the fifth-generation (5G) wireless network, the Internet of Things (IoT) has become a revolutionary technique that enables a diverse number of features and applications. It can able a diverse amount of devices to be connected in order to create a single communication architecture. As it has significantly expanded in recent years, it is fundamental to study this trending technology in detail and take a close look at its applications in the different domains. It represents an enabler of new communication possibilities between people and things. The main asset of this concept is its significant influence through the creation of a new world dimension. The key features required for employing a large-scale IoT are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications. This research work is presented in four main sections, including a general overview of IoT technology, a summary of previous correlated surveys, a review regarding the main IoT applications, and a section on the challenges of IoT. The purpose of this study is to fully cover the applications of IoT, including healthcare, environmental, commercial, industrial, smart cities, and infrastructural applications. This work explains the concept of IoT and defines and summarizes its main technologies and uses, offering a next-generation protocol as a solution to the challenges. IoT challenges were investigated to enhance research and development in the fields. The contribution and weaknesses of each research work cited are covered, highlighting eventual possible research questions and open matters for IoT applications to ensure a full analysis coverage of the discussed papers.

Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.