International Journal of Communication Networks and Information Security (IJCNIS)
Not a member yet
1021 research outputs found
Sort by
An Enhanced Data Clock Synchronization and Equalization Model using Adaptive Pi Sync Method in WSN for Communications
No full textClock synchronization is a major service in WSN field since it can be implemented for data integration based on TDMA approaches. It widely performed because of their ability for monitoring the different situation and the fundamentals of time synchronization methods faced challenged task in wireless sensor network. Clock synchronization using LCM method based on weight transfer protocol in the existing research has explored in a various methods. Clock offset and clock skew has removed with the help of sensor nodes and the proposed model implemented with LCM and PI enabled nodes to occur the network synchronization time by measuring the clock time period to the destination group with different energy based transfer protocols with weight based transfer protocol. Energy based transfer protocol have approached PI sync method to adopt the clock synchronization process with energized clock pulse based input methods. The proposed research model will channelize the data packets with encryption and decryption data source from the source of destination node. The research has executed the higher accuracy with low communication error, communication overhead and reduced delay time
A Hybrid Solution Analysis with IFDMA to Optimizing the Response of the Dense Layers
No full textIn current trends in wireless communication power reduction and capabilities plays major role in detection or estimating the overall consumption at receiver or transmitter. Peak values and mean values or average values in power reduction with in the range of 0-7dB for next generation communication such as 5g, 5g+ or ever 6G. With regards of the power in current mobile communication technologies PAPR feature is still specifying its importance with massive communication domains. To reduce the overall problem of PAPR values for every phased response or frequency ones indicates a solution that have to be realized for effective design in Wireless Network. In order to implement such system, OFDM, LFDMA, IFDMA and other learning models with SLM-OFDM, PSO-OFDM …. etc. In this paper, we introduce a hybrid solution analysis with IFDMA with Hybrid CNN with LSTM structure with more than 9-layers of dense layers indicating the overall optimization response of the Dense layers for the observed optimized design. To realize such problem, we introduce a communication model with very good spectral behaviour and BER performance. With simulation result we demonstrate IDDM algorithm have been shown improved results with PAPR reduction of 4.5 to 6 dB variation based on “n” factors for every simulation response
Sustainable Development Goals: Public Sentiment and Participation Index
No full textThis study investigates the determinants of sustainable development in India, focusing on four key factors: economic development, social equity, environmental preservation, and education & awareness. A cross-sectional survey was conducted across a diverse segment of the population to gauge citizens' knowledge, attitudes, and perceptions regarding Sustainable Development Goals (SDGs) and the 2030 Agenda. The study employed a quantitative approach, utilizing a structured questionnaire and SMART PLS 3 for data analysis, including structural equation modeling (SEM). The findings indicate that all four independent variables significantly influence sustainable development, with education and awareness showing the strongest impact. Economic development, social equity, and environmental preservation also play crucial roles in shaping public acceptance and engagement with sustainability initiatives. The study underscores the importance of a multifaceted approach, integrating governance policies, educational efforts, and socio-economic strategies to advance sustainable development in India. These insights provide valuable guidance for policymakers and stakeholders aiming to promote sustainability and achieve the SDGs in the Indian context
Multisensory Method in the Study of Language Skills of Students with Special Educational Needswith Visual Impairment: A Preliminary Concept
No full textThe factor of academic excellence in primary and secondary school is very relevant to the mastery of reading. Furthermore, the ability to read proficiently is an important skill for academic mastery at a higher level, career and everyday life. Unfortunately, reading difficulty is the main challenge for students in primary and secondary schools, especially special education students. Referring to the International Student Assessment Program (PISA) 2009+ test (Malaysia's first participation) introduced by the Organization For Economic Co-operation and Development (OECD) shows that Malaysia's achievement is at an unsatisfactory level as it is in the bottom third group among 74 countries participants. Malaysia is below the international and OECD average performance (Exhibit 3). Pupils who are 15 years old fail to reach the minimum skill level of almost 60% in Mathematics, while 44% in Reading and 43% in Science
Concept of Early Intervention for Students with Special Education Needs with Visual Impairment
No full textEarly intervention programs are support and help for optimal child development, such as targeted interventions to improve the quality of life of family children, and general contact with support services such as education, health, social care services (Suzi J. Sapiets, Vasiliki Totsika, Richard P. Hastings, 2020). Children with special educational needs should undergo an early intervention program as early as possible. Based on Borhannudin Abdullah and Wan Nomi Wan Omar (2018), early intervention programs can help parents detect their children's disabilities early and intervene to reduce learning barriers caused by their disabilities. Early intervention is to help children's development, functioning and participation in the family and community context, which is in line with the framework of the International Classification of Functioning, Disability and Health in the USA (Damiano, D.L. & Longo, E., 2021). Early intervention is defined as a systematic approach to identify and provide assistance for children before the age of seven with developmental needs in all aspects.
 
Impact of H-Index in Academicians Visibility
No full textPublication metrics are a form of determining the quality of the productivity of publishing scientific articles. When a researcher has published many scientific articles in a certain field, then many people will read it and make it a reference material. Therefore, the taking and reference by other parties of a researcher's scientific articles need to be evaluated metrically to determine the level of productivity of the researcher and also their academic impact. Based on that, publication metrics are created for evaluation purposes.This metric shows the evaluation of the publication results of articles that have been made. This method can also show the visibility or appearance of the writer in a certain field. Through this metric, a person can exist consistently in his field scientifically based on his own field of study. In today's academic world, metrics that are often used are H-index and i10 index. Both of these indexes are widely used by scientific article data storage in determining the productivity and visibility of a writer or researcher. Compared to H-Index which is used by various iliah article data keeper, i10 index is exclusively created and used by Google Scholar data keeper only
Diabetes Type Classification using ANN, KNN, SVM and Decision Tree
No full textReceived: 27 Apr 2024
Accepted: 03 Sep 2024
Diabetic Retinopathy (DR) is a serious diabetic complication and the main cause of blindness globally. Preventing vision loss requires early DR lesion diagnosis and categorization. DR early detection and treatment can significantly reduce the risk vision loss .This paper focuses on classification of a sample into Diabetic & Non-Diabetic, utilizing various techniques such as Decision Tree, ANN, KNN, SVM, Random Forest, Gradient Boosting Algorithms. The NCSU Diabetes dataset is pre-processed, and models are trained and evaluated for accuracy. SVM and ANN achieve over 80% accuracy, highlighting their potential in diabetes type classification. PIMA Indians Dataset is used for reference. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and cost-consuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, Machine learning has become one of the most common techniques that have achieved better performance in many areas like classification. To identify the best classifier for diabetic retinopathy SVM, Decision Tree, Logistic Regression, k-Nearest Neighbors, and ANN classifiers are compared in this paper. Furthermore, the DR available datasets have been reviewed. Various challenging issues that require more investigation are also discussed. Different machine learning algorithms are compared with the previous research and the results are satisfactory. This study improves diabetic retinopathy diagnosis by revealing the efficacy of different machine learning classifiers and helping develop accurate and efficient computer-aided diagnostic systems for early detection and management
Prediction and Segmentation of Heart Disease using Deep Learning Algorithm
No full textIn this new generation of social and economic changes the industries are giving a huge preference for the new technologies of industrial revolution for industrialization. In this period, day by day the technologies are growing very fast. Nowadays, the information’s are used as technology this is known as nothing but knowledge. Here are the combinations of algorithms of data analytics converts the stored data into a knowledge. In this system various machine learning algorithms are used and the data which predicts the patient whether patient is having heart disease or not. The main theme of our project is the heart disease prediction using machine learning algorithm. Models based on supervised machine learning algorithms such as Decision Tree, Support Vector Machine, K-Nearest Neighbor, Logistic Regression. Keywords: Support Vector Machine, K-Nearest Neighbor, Decision Tree, Logistic Regression.  
Efficacité De La Classe Inversée à Améliorer La Performance Langagière En Classe Hétérogène Du Français, 2èmeLangue étrangère (FLE2) Chez Les Apprenants De La Faculté Des langues Et De La Traduction En Arabie Saoudite
No full textCette recherche vise à mesurer l'efficacité de la stratégie de la classe inversée à améliorer la performance langagière en français 2ème langue étrangère chez les apprenants de la faculté des langues et de la traduction à l’Université Islamique de Mohammad Ibn Saoud en Arabie Saoudite. Le chercheur a choisi l'échantillon parmi les apprenants du 4ème niveau universitaire qui étudient le français comme 2ème langue étrangère dans la faculté où le programme de l'étude dans le département de l'anglais se compose de 8 niveaux et l'étude du FLE est pendant le 4ème et les 5èmes niveaux. L’échantillon se compose de 66 apprenants saoudiens et non – saoudiens divisés en 2 groupes, l’un représente une classe traditionnelle (32 apprenants) et l’autre représente une classe inversée (34 apprenants). Le chercheur a préparé les outils de la recherche : un questionnaire sur la nature des apprenants, Une grille d’analyse du contenu,deux tests d’évaluation (écrit / oral) sur trois périodes de l’année universitairepour le but de mesurer la performance langagière des apprenants et une grille d’observation pour le but de mesurer l’interaction (écrite / orale). Après avoir appliqué les outils de la recherche, les résultats ont affirmé qu'il existe une différence statistiquement significative entre la moyenne des notes des étudiants dans la classe inversée (le groupe expérimental) et celle des étudiants dans la classe traditionnelle (le groupe témoin) sur les 2 tests d’évaluation et même sur la grille d’observation en faveur des notes des étudiants en classe inversée. À la lueur des résultats de la recherche, le chercheurprésente les recommandations et les suggestions liées aux résultats précédents. 
Feature Fusion Pyramid Network Cosine Similarity-based Face Recognition for Patient Information Access
No full textPatient information access control has become progressively significant as far as healthcare systems are concerned. It is pivotal to boost healthcare security to circumvent data loss in spite of the numerous security mechanism provided by healthcare management. The gaps required to be addressed using an elaborate secure mechanism that permits users in access the data based on the confidentiality level. In this work, we propose a novel Feature Fusion Pyramid Network Cosine Similarity-based face recognition for patient information access, which uses blockchain along with the Feature Fusion Pyramid Network to mention safety disquiet as well as facilitate choosy sharing of medical documentation between doctors and patients.