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Early detection of cataract is crucial for thwarting visual impairment worldwide, and the utilization of automated cataract detection through medical images has shown increased growth for several years. The automated detection model comprises image processing, feature extraction and classification process to ensure accurate identification of infected cataract eye images. However, the recent detection model endures various challenges, including complex computing requirements, feature redundancy, inadequate precision, generalization and less data diversity. To overcome these challenges, a Chaotic Adaptive Poplar-Bacteria Optimization (Cha-PO) based Cataract VisionNet (CVNet) method is proposed to enhance diagnostic accuracy and operational efficiency. The Cha-PO model is specifically used for optimal feature selection of fundus images by reducing the dimension of the images, which ensures acute diagnostic data preservation. CVNet model used for classifying the cataract images by applying the deep hierarchical learning mechanisms alongside optimized network parameters to boost accuracy levels and operational reliability. The proposed approach is validated using the Eye Cataract Kaggle dataset, and it outperforms the traditional models with 99.10% accuracy, 99% precision, 99.21% recall and 99.10% of F1-Score. With a 99s execution time, it requires fewer computational resources than other baseline models, making it suitable for medical diagnosis.

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.

Energy use and data collection are the main issues in real time wireless sensor network scenarios. Mobile sinks help balance energy usage, reduce multi-hop transmission, and extend network lifetime through moving around the network to collect data at predetermined locations. The proposed novel Rank-Based Path Planning algorithm with Recurrent Neural Networks identifies hotspot nodes based on energy dissemination, network traffic, and multi-hop transmission. Mobile sink only visits the hotspot nodes to collect data, while other nodes forward data to the nearest hotspot. Experimental results shows 35% decrease in energy consumption and 13% increase in network life compared to existing state-of-art algorithms and moderate increases in simulation time, ensuring efficient data collection.

Physical Layer Security (PLS) in Cognitive Radio Networks (CRN) improves the confidentiality, availability, and integrity of the external communication between the devices/ users. The security models for sensing and beamforming reduce the impact of adversaries such as eavesdroppers in the signal processing layer. To such an extent, this article introduces a Secure Channel Estimation Model (SCEM) using Channel State Information (CSI) and Deep Learning (DL) to improve the PLS. In this proposed model, the CSI is exploited to evaluate the channel utilization and actual capacity availability throughout the allocation intervals. The change in channel capacity and utilization augments the need for security through 2-level key shared authentication. The deep learning algorithm verifies the authentication completeness for maximum channel capacity utilization irrespective of adversary interference. This verification follows mutual authentication between the primary and secondary users sharing the maximum capacity channel with high secrecy. The learning monitors the outage secrecy rates to verify failed allocations such that the replacement for allocation is pursued. Thus, the physical layer security between different user categories is administered through maximum CSI exploitation with high beamforming abilities. The proposed model leverages the secrecy rate by 10.77% and the probability of detection by 15.01% and reduces the interference rate by 11.07% for the varying transmit powers.

Quality of Service (QoS) in cognitive radio networks (CRNs) is achieved through fair resource allocation and scheduling for secondary users regardless of channel capacity through multi-channel communications. Fairness index updates are periodic towards multi-user allocations to meet the QoS demands. In this article, a Connected Resource Map-induced Resource Allocation Scheme (CRM-RAS) is introduced. The proposed scheme identifies radio and user resource availability and constructs an allocation map from the primary users. For a periodic allocation interval, the map’s fairness index is updated through maximum resource utilization and QoS factor. This QoS factor is computed based on low latency and high allocation rates that are directly proportional to the fairness index. The fairness index is verified using distributed federated learning that is active between the primary and secondary user terminals. If the fairness index drops below the actual allocation rate, then the scheduling for resource allocation with concurrency is pursued. Based on the improving fairness index through concurrent scheduling the distributed federated learning encourages consecutive radio resource allocation. Thus the process is repeated until the allocation map is confined to a one-to-one connectivity between the primary and secondary users. The proposed CRM-RAS achieves 8.15% high sum rate and 8.88% less error rate for the maximum SNR.

Purpose We propose cloud IoT based LMS (Livestock Management System) with three features. i) Animal healthcare monitoring and recording using IoT sensors via wearable collar, ii) Animal livestock identification using UID for animals (smart tag) and owners (smart card), iii) QR code reading, processing and display of the details in mobile via wireless technologies. Design/methodology/approach The developed animal monitoring device is used to detect animal physiological parameters such as body temperature; physical gestures like sitting, standing, eating and heartbeat, environmental parameters such as air temperature and relative humidity. Also, e-animal husbandry information network management system is the comprehensive web-based animal husbandry software designed for better interaction between veterinary hospital, veterinary doctor, owner, farmer and animal husbandry management. Findings Animal monitoring device mounted on the neck sense the values and predict the health status of the animal by using cloud IoT analytics platform. The accuracy of the system is 90 per cent and it can be well placed in the livestock environment. Research limitations/implications This research is carried out in livestock cows located in Tirunelveli district. The practical difficulty was in placing sensors on the animal. The digital feed from the farmers and the veterinary hospital is input in the animal husbandry management software. Practical implications The developed system can be implemented for monitoring the health status of the animal from anywhere using mobile applications. Also, the digitized animal information helps the government to take the right decisions on policies and fund allocation. Social implications The implemented system can be easily scaled up to large environments by using wireless communication and animal husbandry data will be available immediately. UID scheme for animals can uniquely identify the animal and its details. Originality/value The proposed work implements novel livestock monitoring and analytics system along with Aadhar (Unique ID) for animal. The proposed UID scheme is innovative and unique.

In this article, a 12 switch 31 L multi-level inverter (MLI) is proposed with the benefits of least switching devices for electric vehicle applications. In most electric vehicles (EV), conventional inverters are utilized so the lifetime of electric vehicle induction motors is reduced due to the high THD level and high voltage stress. To rectify this, a new inverter topology is proposed with minimum switching devices by increasing the level to 31, and also the THD should be maintained within IEEE standards. This inverter topology is constructed with variable DC sources (PV system) along with required capacitors and a polarity changer. The specific feature of this topology is that it can generate any level of voltage with minimum switching devices, less voltage stress, minimum THD, and less cost are tabulated in the comparison. This type of inverter can also applicable for high voltage (HV) applications and grid-connected systems. Furthermore, the proposed topology is simulated with MATLAB software and the downscale prototype model is developed using the DSPIC30f2010 controller.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman’s premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

Vulnerability assessment and mapping is a significant tool for sustainable management of the precious natural groundwater resources. DRASTIC is an extensively used index model to map groundwater vulnerable zones. However, the original DRASTIC model rates and weights used in most of the research depict the poor correlation between nitrate concentration and groundwater vulnerability index. Wilcoxon test and five population-based metaheuristic (MH) algorithms, namely, firefly algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), shuffled frog leaping algorithm (SFLA), and particle swarm optimization (PSO), were used to optimize the rates and weights of the DRASTIC model to improve its accuracy. The performance of all the employed metaheuristic algorithms converges to a global optimal solution at different iterations, and to choose the best algorithm for DRASTIC weights optimization, a ranking methodology was proposed. The algorithms were ranked by calculating the relative closeness of alternatives with computational speed and the number of iterations as attributes in the TOPSIS method. This study identifies FA as the outperforming algorithm among the employed for this specified weight optimization problem based on ranking. The result of the optimization model proposed depicts significant improvement in the correlation coefficient between the groundwater vulnerability index and nitrate concentration from 0.0545 for the original DRASTIC model to 0.7247 for the Wilcoxon-MH- DRASTIC. Hence, this ranking approach can be adopted when global optimal solution is found by all employed algorithms in DRASTIC weight optimization.

The CuS@reduced graphene oxide (CuS/RGO) hybrid nanocomposite was synthesized by facile hydrothermal method and used as a photoelectrode material in photovoltaic applications. In the hydrothermal route, RGO is formed by the reduction of GO with simultaneous formation of CuS/RGO nanocomposites. The CuS/RGO nanocomposites was investigated using powder XRD, TEM, HR-TEM, Raman, XPS, DRS UV–Vis spectroscopy, Photoluminescence (PL) measurements. XRD and TEM results suggest that CuS crystalline with individual spherical like homogeneous nanoparticles sizes in the range of 45–35 nm, which is distributed throughout the RGO sheets. We further construct the flexible photoelectrodes by using CuS and RGO and studied the photovoltaic performance. Photovoltaic parameters, such as short-circuit photocurrent density, open circuit voltage, fill factor and conversion efficiency were found to be 16 mA/cm 2 , 0.71 V, 70.1% and 7.81% respectively, for CuS/RGO photoelectrode. The improved photo conversion efficiency of CuS/RGO is due to enhancing the electronic injection ability and reducing the photogenerated charge recombination. These photovoltaic results indicate a simple methodology for the low cost and effortless synthesis of an alternative CuS/RGO photoelectrode in high performance photovoltaic devices.