International Journal on Recent and Innovation Trends in Computing and Communication
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Developed A Hybrid Optimal Feature Vector Selection with Blockchain Technology for Smart Healthcare 4.0
Get PDFThe health economy has been an innovative technology since time-honored. Preserving and maintaining patient data are essential in a routine life. Patient’s medical information is very important for every individual not only for patients but also for doctors who are examining them. Advances in sensing technology, processing of data, and communication protocols have transformed the healthcare industry. Patients, physicians, hospitals, and other stakeholder may keep vital data and medical records with the use of electronic healthcare records (EHR). The goal of research should be to develop a Hybrid Optimal Feature Vector Selection with Blockchain Technology (HOFVS-BT) for smart healthcare 4.0 to improve the secure transmission of data which is supported intelligent IoT and medical detection platform possible. For Feature vector selection, proposed an Orthogonal Wolf Optimization (OWO) algorithm. Furthermore, safeguarding private patient details is taken into account by establishing an upgraded Blockchain-based IoT data security solution that not only secures the data, but also fosters trust between patients/users and healthcare service providers
Laser based Optical Wireless Communication using Log Normal Distribution and SIM BPSK Modulation
Get PDFLaser based Optical Wireless Communication(OWC) is emerging as one of the key advancements for the 5G and beyond technologies. This technology provides high bandwidth, extremely high data rate and high speed communication due to the use of optical waves for data transmission in the field of wireless communication. The system performance is limited due to the presence of turbulence in the atmosphere.We have developed system model using log normal channel distribution in the weak to moderate atmospheric turbulence. Closed form of expression is derived which provides the channel capacity in the presence of weak turbulence using log normal distribution. Subcarrier Internsity Modulation (SIM) BPSK modulation technique is used in OWC system which provides bettter performance in terms of received BER as compared to NRZ – OOK modulation
Power Reduction Sleep Scheduling Technique for Cloud Integrated Green Social Sensor Network
Get PDFThe wireless sensor network is the maximum appropriate technology nowadays with such awesome applications and areas including Infrastructure tracking, environment tracking, health care tracking, etc. Cloud Computing has fantastic data collecting skills and effective data processing ability. Social Network is a group of People or organizations of human beings with similar intentions. Social Sensor Cloud is one type of expertise-sharing mechanism wherein similar types of human beings can connect. Energy Consumption is nowadays the largest challenge as far as the concern with green environment. Because the battery life of the sensor is so limited, the Social Sensor Cloud must be energy efficient. As a result, this article will concentrate on Energy-Efficient Techniques for the Social Sensor Cloud. According to our findings, findings, the majority of energy-saving measures will cope with not unusual place Parameters including Network Lifetime, Network Work rate, Throughput, Energy, Bandwidth, etc. We will Summarize current Technology and we Will Provide Our Architecture for Energy Reduction in Social Sensor Cloud
Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning
Get PDFIn the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches
Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases
Get PDFWith the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain
An Effective Supply Chain Model using Blockchain in IoT with Trust Enabled Hybrid Concensus Algorithm
Get PDFWith the rapid growth of multiagent systems, concerns about privacy and security have assumed a position of paramount importance. Blockchain technology has gained a lot of attention since it first appeared because of its benefits in terms of decentralization, accessibility, traceability, and the capacity to be trustless. The increasingly complicated supply chains in today's world face significant issues regarding traceability and integrity. Blockchain technology holds out the possibility of developing a new concept for supply chain traceability, lapping these worries. Thus, in this research, the trust enable hybrid consensus algorithm is proposed for establishing consistency in the network with the irrelevant traders. The authorization is identified by the proposed model for accessing the Blockchain network depending on the accessibility rules. Depending on the various information sources, the authenticity of data is calculated, which makes the interaction between both the agent as well as the resource. The efficiency of the proposed model is determined by three different measures for secure data transfer. The attained minimal transaction time is 0.856 ms, memory usage is 87.684 KB, and responsiveness is 3.599 ms, respectively
An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective
Get PDFThe burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care
Automated Pet Water Consumption Monitoring System
Get PDFMore than half of all people on the planet voluntarily keep pets as companions, but these animals are starting to strain their owners. While feeding their pets, owners experience a lot of stress. One of the pet feeders that may be managed online through a mobile application is the IoT pet feeder. This paper details an IoT based monitoring system which automates the process of detecting and replacing dirty water in a pet’s bowl in a cheap manner. It also describes methods of tracking the overall water consumption of the pet via the system and monitoring the pet’s health via detection of consumption anomalies
A Novel Communication Pathway Metric Evaluation using Throughput and Energy Improvements over Wireless Sensor Networks
Get PDFLifetime connectivity and low power consumption are two requirements for WSNs. Additionally, increasing WSN commercialization, application monitoring for higher QoS. Maintaining the improvement of the wireless sensor networks effectiveness requires the establishment of the energy-efficient and consistent connection. In order to increase the efficiency of routing algorithms in latest technology assisted WSNs, a novel method is developed. The gadgets with the lowest power consumption are carefully selected for the appropriate use and carefully managed. The goal of this research is to develop low-power routing algorithms for use in WSNs. Numerous sectors, including the military, the medical profession, surveillance of observing, public transportation sectors etc., can benefit from wireless sensor networks deployed with a large number of mobile nodes in a communications system. To examine data from nodes that are freely moving about the zone, we developed and simulated a wireless network system based on the Distributed Internet of Things. The proposed method of Intelligent Route Metric Analyzer (IRMA) improves upon the efficiency of the standard algorithm with regards to energy consumption. In addition, the ground wireless sensing design's ideal trajectory is identified using a realistic tempering approach. In the end, this concept is compared to similar ones like AODV and the findings reveal that it performs better with regard to of energy consumption, delay and other important metrics. According to the paper we analyzed, the primary objective was to devise a routing method that would sustain network operation for as long as feasible by decreasing the energy needed for different operations at each individual sensor node while keeping the total energy consumption of the nodes constant over their lifetimes. Finally, the study makes a contribution to the ongoing discussion of the difficulties in developing a routing protocol for WSN, taking into account the interdependency of different network factors. Median throughput, median network latency, and standardized route load are only few of the efficiency metrics addressed in this article
Cyber security Reinforcement through Firewall Log Analysis and Machine Learning
Get PDFFirewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT, MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00%. This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate a more dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.