IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet

We come across hospitals and non-profit organizations that care for people with paralysis who have experienced all or portion of their physique being incapacitated by the paralyzing attack. Due to a lack of motor coordination by their mind, these persons are typically unable to communicate their requirements because they can speak clearly or use sign language. In such a case, we suggest a system that enables a disabled person to move any area of his body capable of moving to broadcast a text on the LCD. This method also addresses the circumstance in which the patient cannot be attended to in person and instead sends an SMS message using GSM. By detecting the user part's tilt direction, our suggested system operates. As a result, patients can communicate with physicians, therapists, or their loved ones at home or work over the web. Case-specific data, such as heart rate, must be continuously reported in health centers. The suggested method tracks the body of the case's pulse rate and other comparable data. For instance, photoplethysmography is used to assess heart rate. The decoded periodic data is transmitted continually via a Microcontroller coupled to a transmitting module. The croaker's cabin contains a receiver device that obtains and deciphers data as well as constantly exhibits it on Graphical interfaces viewable on the laptop. As a result, the croaker can monitor and handle multiple situations at once

Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.Comment: 13 pages, 6 figure

Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses

Screening of marine sponge-associated bacteria from Echinodictyum gorgonoides and its bioactivity

This paper describes the evaluation of bacterial strain (MB2) bioactivity isolated from marine sponge. The sponge Echinodictyum gorgonoides associated bacterial strain MB2 was tested for its action against various human pathogenic bacterial isolates. The biochemical tests were done to determine the characterization of the bacterial strain and to identify the specific MB2 strain. The cytotoxicity studies of the bacterial strain (MB2) isolated from the sponge was analysed using the Brine shrimp cytotoxicity assay and confirmed against the glioma C6 cell lines using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The availability of proteins were analysed by using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis. Finally, the isolated bacterial MB2 strain from the marine sponge E. gorgonoides was identified through means of 16sRNA sequence analysis. It was then confirmed by means of basic local alignment search tool (BLAST). From these results, it is confirmed that the strain MB2 is Staphyloccous sp. Hence it is assumed that the sponge associated proteins and the presence of secondary metabolites could be the source for determining various biological activities.Key words: Echinodictyum gorgonoides, glioma C6 cell lines, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, 16s rRNA

Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing

With the onset of the Information Era and the rapid growth of information technology, ample space for processing and extracting data has opened up. However, privacy concerns may stifle expansion throughout this area. The challenge of reliable mining techniques when transactions disperse across sources is addressed in this study. This work looks at the prospect of creating a new set of three algorithms that can obtain maximum privacy, data utility, and time savings while doing so. This paper proposes a unique double encryption and Transaction Splitter approach to alter the database to optimize the data utility and confidentiality tradeoff in the preparation phase. This paper presents a customized apriori approach for the mining process, which does not examine the entire database to estimate the support for each attribute. Existing distributed data solutions have a high encryption complexity and an insufficient specification of many participants' properties. Proposed solutions provide increased privacy protection against a variety of attack models. Furthermore, in terms of communication cycles and processing complexity, it is much simpler and quicker. Proposed work tests on top of a realworld transaction database demonstrate that the aim of the proposed method is realistic

Screening of marine sponge-associated bacteria from Echinodictyum gorgonoides and its bioactivity

This paper describes the evaluation of bacterial strain (MB2) bioactivity isolated from marine sponge. The sponge Echinodictyum gorgonoides associated bacterial strain MB2 was tested for its action against various human pathogenic bacterial isolates. The biochemical tests were done to determine the characterization of the bacterial strain and to identify the specific MB2 strain. The cytotoxicity studies of the bacterial strain (MB2) isolated from the sponge was analysed using the Brine shrimp cytotoxicity assay and confirmed against the glioma C6 cell lines using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The availability of proteins were analysed by using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis. Finally, the isolated bacterial MB2 strain from the marine sponge E. gorgonoides was identified through means of 16sRNA sequence analysis. It was then confirmed by means of basic local alignment search tool (BLAST). From these results, it is confirmed that the strain MB2 is Staphyloccous sp. Hence it is assumed that the sponge associated proteins and the presence of secondary metabolites could be the source for determining various biological activities

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors

Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET

Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput

A system of remote patients' monitoring and alerting using the machine learning technique

Machine learning has become an essential tool in daily life, or we can say it is a powerful tool in the majority of areas that we wish to optimize. Machine learning is being used to create techniques that can learn from labelled or unlabeled information, as well as learn from their surroundings. Machine learning is utilized in various areas, but mainly in the healthcare industry, where it provides significant advantages via appropriate decision and prediction methods. ,e proposed work introduces a remote system that can continuously monitor the patient and can produce an alert whenever necessary. ,e proposed methodology makes use of different machine learning algorithms along with cloud computing for continuous data storage. Over the years, these technologies have resulted in significant advancements in the healthcare industry. Medical professionals utilize machine learning tools and methods to analyse medical data in order to detect hazards and offer appropriate diagnosis and treatment. ,e scope of remote healthcare includes anything from tracking chronically sick patients, elderly people, preterm children, and accident victims.The current study explores the machine learning technologies’ capability of monitoring remote patients and alerts their current condition through the remote system. New advances in contactless observation demonstrate that it is only necessary for the patient to be present within a few meters of the sensors for them to work. Sensors connected to the body and environmental sensors connected to the surroundings are examples of the technology available.Campus At

Antifungal and Cytotoxic Activities of Some Marine Sponges Collected from the South East Coast of India

The present work describes the biological activities using the marine sponges collected from kanyakumari. The sponges are such as the Callyspongia diffusa, Echinodictyum gorgonoides, Callyspongia reticutis, Gelliodes cellaria, and Thalysias vulpine. It is revealed that the sponges showed the antifungal activity against the various fungal strains such as the Aspergillus niger, Pencillium notatum,and Candida albicans by using the agar well diffusion method. The sponge crude extracts seems to have effective cytotoxic property that was detected by Brine shrimp assay. Hence it is assumed that the marine sponges act as the vital source for the development of anticancer drugs