International Journal on Recent and Innovation Trends in Computing and Communication
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Patient Prognosis based on Lung Nodule Detection: A Review and Prediction Using Machine Learning and Deep Learning Techniques
Full text linkLung Cancer Integrating Pathological and Radiological Features is a strong and descriptive title that effectively communicates the core aspects. It emphasizes the fusion of different data sources using a neural network approach for the purpose of understanding and predicting lung cancer outcomes. It's clear and concise, which is ideal for a project title. In this study, we provide a summary of the latest CAD methodologies that use deep learning to pre-process, segment, classify, and retrieve lung nodule data from CT scans, in addition to reduce false positives. Up to November 2020, academic conferences and publications were the source of a selection of articles. We go over the history of deep learning, go over some key points about lung nodule CAD systems, and evaluate the effectiveness of the chosen research over a range of datasets. Researchers and radiologists are able to acquire a better understanding of computer-aided design machine learning as well as deep learning methods for the detection, segmentation, classification, and retrieval of pulmonary nodules by reading this review. We review the effectiveness of existing methods, discuss their drawbacks, and suggest future lines of investigation for high-impact research
Towards AI-Driven Standardization in Disease Indication: Implementing Controlled Vocabulary for Clinical Reporting Systems
Full text linkThe standardization of disease indications in clinical reporting systems using AI-driven approaches is examined in this research. The evaluation assesses the accuracy, efficiency, scalability, and clinical usefulness of NLP approaches such as Named Entity Recognition (NER), Entity Linking, Supervised Machine Learning (SVM), Unsupervised Machine Learning (K-means), and Ontology-Based Approaches. The study emphasizes the advantages of each technique and its function in representing structured clinical data
Heart-Based Biometric Authentication
Full text linkHeart-based biometric authentication is a cutting-edge technology that utilizes the unique characteristics of an individual's heart to verify their identity. This innovative approach to authentication has gained significant attention in recent years due to its high level of accuracy and security. In this analytical paper, we will explore the concept of heart-based biometric authentication, its advantages and limitations, and its potential applications in various industries
A Study on the Statistical Analysis of the Geomagnetic Storm of January 2021 and its Effect on the Ionosphere.
Full text linkThe twenty fifth solar cycle is in progress and is it expected in peak in 2025. More and more geomagnetic storms will be expected in the coming period. During a geomagnetic storm event a lot of energy is deposited in the magnetosphere and eventually in the ionosphere. In this paper we present the results of a minor geomagnetic storm that occurred on January 25, 2021. The solar wind data and geomagnetic data for the storm are studied. Significant changes in the solar and geomagnetic parameters were observed in association with the storm. All the parameters are statistically analysed to extract the storm time behaviour and also study the process of ionospheric magnetosphere coupling during the storm. For this we have selected taken the average of ten quiet days and statistical comparison with the disturbed days has been done
Proposition of a Novel Multipath-Routing Protocol for Manets Connected Via Positioning of UAVS Using Ant Colony Optimization Meta-Algorithms
Full text linkIn the forthcoming operational theatre, combat radio nodes will be strategically positioned to facilitate a myriad of manoeuvres, constituting a dynamic mobile ad-hoc network (MANET), where communication among participating nodes is achieved collaboratively without fixed base stations. However, due to the nodes' mobility, the cohesive formation may fragment into smaller clusters, while conversely, multiple smaller groups might amalgamate into larger entities. In such a dynamic milieu, the integration of unmanned aerial vehicles (UAVs) emerges as a potent solution to enhance network coverage and connectivity among disparate groups. Sending of information all over the MANETs is dependent mostly on methodologies of routing, where the on-request unitary paths procedures to route like AODV and AOMDV (which stands for routing via multiple roads) play crucial roles. Leveraging authentic topographic data becomes imperative to ascertain precise connectivity metrics among nodes, while devising an efficient resource allocation strategy for reliable communication via UAVs warrants attention. Given the predominance of line-of-sight links between UAVs and ground nodes, substantial traffic is anticipated despite less amount of information sectional resources. Furthermore, diverse quality-of-service requirements of network traffic necessitate prioritization based on tactical imperatives. In these studies, formulations have been done for Unmanned Flying Vehicle localizing problems geared towards maximal connectivity inside groups along with information section allocating problems aimed at increasing utilities of GC to maximum levels, demonstrating superiority over conventional methodologies through numerical analysis validating the efficacy of our proposed scheme. Wireless connections implemented rapid growths in recent times essentially network of MANET, showcasing significant developments of science and technology
Techniques and Approaches of Facial Recognition under Occlusion: A Review
Full text linkA human face is one of the most prominent features used in the process of authenticating technical applications in the domains of security, biometrics, surveillance and forensics. Recognition and detection of facial features has thus become challenging due to problems of occlusion, emotion, image resolution, varying facial expressions and aging. Such attributes tend to have a great impact on the overall performance of a robust facial recognition system. Hence, facial recognition with presence of occlusion triggers to be a hindrance in the natural environment and thereby limits the system model to recognise faces. For this purpose, multiple research authors have inhibited strategies and techniques to address the issues of occlusion. Numerous developments in the field of machine learning and deep learning have constantly evolved with complex architectures that could design the model from scratch and perform image processing to attain maximum efficiency. Such approaches have the potential to accomplish highest state-of-the art accuracy by minimizing error loss. Nevertheless, facial recognition that tends to bypass occlusion is still imperative to limitations for real?world applications. Hence in this review paper, the authors highlight various problems that a facial recognition system with occlusion might face and thereby proposes to analyse various methods of recognition in order to cope with the existing problems. The paper also focuses on extraction approaches thus used present the novelty. The review finally ends, with a mention of future challenges with regards to occluded facial recognition
Feature Extraction using Singular Spectrum Analysis: Characterizing Dominant Modes for Time Series Forecasting
Full text linkThis study explores the application of Singular Spectrum Analysis (SSA) for feature extraction from the dominant modes of an industry sector. These modes are hypothesized to encapsulate the underlying market trends and cycles, offering an enhanced understanding of stock price dynamics. The methodology involves identification of dominant modes of historical stock price data from leading semiconductor companies, and applying Singular Spectrum Analysis (SSA) to identify and isolate the relevant features contributing to price dynamics. Finally, the features extracted are used to forecast a new time series in the same sector using Elastic Net Regression. The forecasting evaluation metrics indicates lower error rates and high predictive accuracy
A Novel Big Data Approach Using Fuzzy Rule Based Multilayer Perceptrons
Full text linkThey are faced with immense quantities and high velocity of data with complicated structures in the big data era. Social networks, sensors, online and offline transactions, and our daily lives can all produce data. When big data is processed correctly, it can lead to relevant, helpful and useful decisions being made in a number of areas, including government, business, management, and medicine and healthcare. Large amounts of data on healthcare have the ability to significantly enhance patient outcomes, predict epidemics, provide insightful information, prevent diseases that may be prevented, reduce the cost of healthcare delivery, and generally increase life. Big data is made up of patient data that is gathered for remote healthcare applications that differs in terms of volume, velocity, variety, veracity, and value. Healthcare data classification presents a number of challenges for big data since it gathers huge quantities of data. Processing a heterogeneous collection of this size requires a specialized approach, making it one of the most difficult challenges. The paper presents a novel big data approach using fuzzy rule-based multilayer perceptrons to address these problems. Big data offers the ability to accumulate, analyze, manage, and integrate large amounts of disparate, structured, and unstructured information generated by the healthcare systems of currently. A FRCNN (Fuzzy Region based Convolutional Neural Network) classifier is designed to perform normal and disease classification. Accuracy, precision, recall, and F1-score are only some of the performance criteria used to evaluate this model
SIOTEHR: Secure IoT based EHR Scheme in Blockchain Ecosystem
Full text linkIn the present era, the number of illnesses and traumas is steadily rising. In addition, COVID-19 is still spreading in waves. Therefore EHRs (Electronic Health Records) has become a necessity now which can be used to access a patient's prior medical records through the EHR system. India is still falling behind the rest of the globe in the adoption and use of EHR. The IoT-based EHR aids in patient monitoring and enables the doctor to treat the patient right away if necessary. The goal of the EHR system will fail if the central server, which was the system's defined point of failure, fails or is compromised. In this situation, security is a major concern. In this work, we suggest adopting the blockchain technology to resolve this. We created a SIoTEHR (Secure IoT Based EHR) system that is fully decentralized, secure, traceable, auditable, private, and trustworthy by utilizing a private Ethereum Blockchain
Functional Brain Connectivity Differences between Aphasic and Neurotypical Brains
Full text linkAphasia is a language disorder that can arise from brain damage, leading to difficulties in understanding, generating, or using language. Although the precise neural mechanisms are not fully elucidated, it is hypothesized that these disruptions involve altered communication and interaction among brain regions. In this study, functional magnetic resonance imaging (fMRI) was employed to assess functional connectivity in both individuals with aphasia and neurotypical individuals. Functional connectivity is a measure of the way that brain regions communicate and interact with each other. The study participants performed a series of language-processing tasks, while their fMRI data was collected. The study's findings showed that individuals with aphasia had unique functional brain connectivity patterns when compared to neurotypical individuals. These distinctions were most prominent in the left hemisphere, which is conventionally associated with language processing. In particular, individuals with aphasia demonstrated diminished functional connectivity between the language regions in the left hemisphere and other brain regions, including those in the right hemisphere and the frontal lobe. The study's findings suggest that differences in functional brain connectivity may contribute to language deficits in aphasia. The study's findings also hold significant implications for advancing our understanding of the neurological underpinnings of aphasia and the potential for improved diagnostic and therapeutic methods for individuals with this condition