International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Next-Generation Wireless Communication: Exploring the Potential of 5G and Beyond in Enabling Ultra-Reliable Low Latency Communications for IOT and Autonomous Systems
No full textThe current study aims at exploring the development of wireless communication technologies especially 5G and the future 6G to deliver ULLC for IoT and auto-mobiles. By the use of simulation models and real-life examples the research assesses gains resulting from these next generation networks. The outcome indicates that 5G network means a set of numerous improvements as compared with the previous technologies and it possesses the latency of 1. 2 milliseconds and the throughput of 10 Gbps. In the future, 6G technologies have been expected to increase performance even more as the forecasted latency of 0. 8 milliseconds, packet loss rates getting down to around 0. 01%, and throughput which could go to up to 15 Gbps. The study also presents artificial intelligence, the edge computing system, and other high-advanced beam-forming technologies that assist in enhancing network performance and dependability. Another actual example showed how 5G can be used in the control of traffic, which reached a latency of 1. 1 millisecond with the reliability rate of more than 99 %. 98%. Essentially, these research discoveries indicate how next generation wireless networks may revolutionize key applications as well as progress the way toward more reliable connections
A Comprehensive Auto ML Solution for Automated Data Preprocessing and Model Deployment
No full textAn important turning point in the field of machine learning has been reached with the convergence of data preparation and automated machine learning (AutoML). AutoML has become a reliable solution for tackling major issues with data preprocessing approaches because of its capacity to automate the coordination of different machine learning processes. This study covers a wide range of important topics related to data preparation, including feature selection, time- series preprocessing, manual encoding mistakes, class imbalance, and inefficient hyperparameters. AutoML's revolutionary effect on simplifying crucial data preparation procedures is one of its main contributions to data preprocessing. Data preparation has historically been a labor-and time-intensive procedure that calls for specialised knowledge and physical involvement at different points in the process. But many of these jobs may now be completed automatically because to the development of automated algorithms, which has significantly increased productivity and efficiency. Furthermore, by making data preprocessing more approachable for both specialists and non-experts, AutoML has democratised the field. Through the automation of intricate processes like feature selection and hyperparameter tweaking, AutoML technologies enable users to concentrate on more advanced parts of model creation, such formulating problems and interpreting outcomes. In addition to quickening the rate of invention, this democratisation of data preprocessing encourages increased cooperation and knowledge exchange within the machine learning community
Hybrid Machine Learning and Deep Learning Models for Efficient Detection of Arrhythmia from ECG Data
No full textArrhythmia, in its various forms, causes health issues worldwide. Traditional methods for diagnosing arrhythmia can help detect abnormalities in the heartbeat and provide necessary medical intervention. With the advancement of AI, it has become possible to analyze ECG data and detect different types of arrhythmia. Many researchers have contributed to developing ML and approaches for automatic arrhythmia detection. It has been observed from the literature that developing hybrid models using both ML and DL techniques can enhance performance in arrhythmia detection for developing a Clinical Decision Support System (CDSS). We suggest a hybrid technique in this study that combines ML and DL models, followed by a combination of deep learning models, to explore hybrid models in the arrhythmia detection process empirically. We introduce an algorithm called Hybrid Learning-based Efficient Arrhythmia Detection (HLEAD). Our empirical study with the benchmark dataset MIT-BIH revealed that the proposed hybrid models outperformed many existing arrhythmia detection models with the highest accuracy of 99.02%. Therefore, it is suggested that the proposed system developed based on hybrid DL and ML models could be integrated with healthcare applications to implement a CDSS for screening arrhythmias
A Comprehensive Review of Traffic Congestion Evaluation Methods and Approaches
No full text
Traffic congestion stands out as a highly conspicuous, widespread, and pressing transportation issue that affects communities worldwide on a daily basis. The escalation of traffic congestion has emerged as a significant challenge in urban transportation. This phenomenon not only leads to the squandering of precious time but also exacerbates fuel consumption, the emission of harmful pollutants, and poses health risks such as lung diseases and heightened blood pressure. Moreover, it inflicts a toll on workplace productivity. The surge in population numbers, coupled with escalating urbanization rates, inadequate or poorly planned transport infrastructure, deficient public transit networks, and the proliferation of private vehicles, are among the principal factors contributing to congestion. This paper aims to examine and synthesize the insights gleaned from various studies focused on the phenomenon of road traffic congestion.Further,several metrics for measuring traffic congestion have been deliberated upon, categorized into three main types: (a) Travel time-based metrics, (b) Speed-based metrics, and (c) Level of service-based metrics. Additionally,the methods used to collect data on congestion in various research have been reviewed.The study's results underscore the significance of enhancing traffic management and control mechanisms, bolstering public transportation services, allocating greater funds to transport infrastructure development, leveraging modern technological solutions, and fostering cohesive coordination between transportation and land-use policies as crucial factors in alleviating congestion
Effect of nursing knowledge on heat exposure risk in Mecca health centers in 2024
No full textAlthough classic heat exposure and heat stroke are among the oldest known human diseases, their early clinical manifestations, natural history, and complications remain poorly described. Heat exposure and heat stroke are life- threatening conditions characterized by a rapid increase in core body temperature to above 40°C and neurological changes such as delirium, seizures, or coma following exposure to extreme heat alone or in combination with strenuous physical exertion. Heat-related illnesses (HRIs), such as heat stroke (HS) and heat exhaustion (HE), are common complications of the Hajj. The Saudi Ministry of Health (MOH) has developed guidelines for the management of HRIs to ensure the safety of all pilgrims. Medical staff must follow the latest national guidelines for the management of HRIs before and during hospitalization. Effect of nursing knowledge on heat exposure risk in Mecca health centers in 2024. A descriptive cross-sectional study was conducted among nurses to investigate the risk of heat exposure and prevalence of heat-related illnesses among pilgrims who visited primary health care centers inMakkah from May 1, 2024 to May 30, 2024. The total sample size of participating nurses was (200). Relationship between nurses' knowledge of heat exposure hazards and prevalence of heat-related illnesses among pilgrims The relationship between the knowledge level of most participants was general knowledge (56.0%) followed by high knowledge (26.0%) but weak knowledge (18.0%) and total knowledge(100.0%), with significant relationships at P value <0.001 and X2 48.16. Conclusion: Heat exposure and heat illness are not common problems for Saudi Arabians. However, they are significant for pilgrims from other parts of the world during the Hajj season, which varies according to the lunar year. In recent years, the Hajj timing coincides with the summer months of July and August. The average temperature during the Hajj reaches 54 °C (130 °F)
The Revolution of Quantum Computing: Analyzing Its Effects on Cryptographic Security and Algorithmic Efficiency
No full textThis study examines how quantum computing influences cryptographic security and algorithmic performance. Quantum computers, using qubits, superposition, and entanglement, present dangerous levels of risks to traditional cryptography and show promising potential for calculated speed. For predicting secure and efficient quantum computing, the current study adopts some machine learning models such as Decision Trees, Random Forests, and K-nearest neighbours. The Decision Tree model is the most accurate model with 100% precision, and there is a need to incorporate studies and research on quantum-safe algorithms and post-quantum cryptography for potential risks brought by quantum in the future
Analyzing the Behavior of Software Reliability Execution Time Models for Different Agile–Scrum Based Projects
No full textSoftware reliability is an essential component of software development stages. The reliability of a software system plays a vital role in the overall development and success of that software. Reliability is broad and linked to other areas of technologies and approaches. This requires a proactive mechanism, which includes not only technical aspects but also legal and ethical considerations. Maintaining reliability in Agile-based software is an arduous exercise. This happens because in an Agile-based project, frequent changes in requirements are expected during the development cycle. To develop high levels of reliability in an Agile environment, the Quality Assurance (QA) engineer needs to carefully select the appropriate reliability model. In this research paper, we studied the performance of two most popular reliability models namely Basic Execution Time Model and Logarithmic Poisson Execution Model. Since the basic idea behind developing software is quite different in an Agile environment compared to traditional software development processes; a dataset of 30 Agile-based projects has been prepared for the purposes of calculation. These 30 projects in this dataset are divided into three groups as low, medium and high-level projects based on the number of sprints required to complete the work. This paper presents a comparative analysis of these two reliability models on various parameters. As a result, we found that the Logarithmic Poisson Execution Model produces optimal results for most Agile-based projects in all 3 project categories
Student Online Education Adaptability Prediction using Machine Learning Algorithm
No full textThe abstract summarizes the key findings and methodology of the study conducted on various machine learning classifiers for predictive analysis on a specific dataset. Through comprehensive exploration, classifiers like Decision Tree, Random Forest, and XGBoost emerged as high performers, achieving an average accuracy of around 90% with minimal misclassifications. Conversely, classifiers such as Logistic Regression, Gaussian Naive Bayes, and Multi-layer Perceptron demonstrated lower accuracies, indicating their limited suitability for the dataset. Additionally, hyperparameter tuning of the Decision Tree model using a grid search method led to a significant improvement in accuracy to approximately 90.87%. These findings underscore the critical role of thoughtful model selection, evaluation, and optimization in developing accurate and reliable machine learning models for real-world applications
Enhancing English Language Proficiency through Mobile Language Learning Apps: A Comprehensive Overview
No full textBackground and Aim: The increasing importance of Englishlanguage proficiency has become a significant challenge intoday’s globalized world, where effective communication isessential across various domains. This article addresses thepressing need for innovative solutions to enhance Englishlanguage learning, particularly through mobile applications. Theaim of this study is to explore the pivotal role that mobilelanguage learning apps, such as Cake, Babbel, and Elsa, play inmeeting this need. The author aims to highlight the features,target audiences, and language offerings of these applications,emphasizing the critical role of student motivation in thelanguage learning process. Search Method: In conducting thisreview, a comprehensive search was performed using recognizeddatabases and search engines. Keywords related to mobilelanguage learning applications, English proficiency, and userengagement were utilized. The search was limited to articlespublished between 2011 and 2024, adhering to internationallyrecognized guidelines in language education. This approachensured a thorough examination of relevant literature and theinclusion of diverse perspectives. Results: The findings from thereview reveal significant insights into the effectiveness of mobilelanguage learning apps. These applications demonstrate apositive impact on language acquisition, particularly throughfeatures such as gamification, personalized learning paths, andinteractive exercises. Empirical evidence from case studiesunderscores the effectiveness of these tools in improving Englishskills among learners. Conclusion: In conclusion, the challengessurrounding English language proficiency necessitate innovativeapproaches to language education. The results of this studyindicate that mobile language learning applications caneffectively enhance student engagement and motivation. Byfostering collaboration among various stakeholders in thelanguage learning community, this article aims to encouragereaders to consider the implications of these findings. Thepurpose of this study is to compare the advantages anddisadvantages of the three software applications based onprevious literature, providing a nuanced understanding of theireffectiveness. The methodology employed involved a thorough review of articles publishedfrom 2011 to 2024, ensuringa comprehensive analysis ofthe topic
Repeated Dexmedetomidine Infusion is a Two-shot Weapon for Pain and Pain-induced Mood Disorders in Chronic Pain Patients: A Placebo-controlled Randomized Prospective Interventional Study
No full textObjectives: This prospective study examined the effects of 6-sessions of dexmedetomidine (DEX) for patients had chronicmusculoskeletal pain (CMSP) on the frequency and severity ofpain, and pain-induced depression, anxiety and kinesiophobia.Patients & Methods: 80 CMSP patients were evaluated usingthe short-form McGill Pain Questionnaire (SF-MPQ), PainAnxiety Symptoms Scale (PASS), State-Trait Anxiety Inventoryfor measuring state and trait anxiety, short-form Tampa Scale ofKinesiophobia and Beck Depression Inventory-II. Patients wererandomly divided into Group-C received placebo infusion andGroup-S received DEX infusion (0.7 ?g/kg for 1-hour) twiceweekly for three weeks. Evaluations were re-assessed at the endof infusion (T2), 1-m (T3) and 3-m (T4) in comparison to scoresdetermined before start of infusion therapy (T1).Results: At T2-T4 the scores of all the evaluated toolsdecreased significantly in Group-S compared their T1 scores andto scores of patients of Group-C. Moreover, 47.5% of Group-Spatients were independent on any analgesia since T2 till T4 withsignificant difference compared to Group-C patients and to theirconsumption rate and type of analgesia at T1. Satisfaction scoresof Group-S patients by the infusion therapy were significantlyhigher compared to that of patients of Group-C and to their T1scores by the usual analgesia. The re-assessed scores werenegatively correlated with the administration of DEX infusionand positively correlated with the decrease in pain scores. ROCcurve analysis defined decreased kinesiophobia scores as thesignificant predictor for the decreased depression scores to 0-13.Conclusion: DEX infusion might break the circle of painpsychopathy-poor quality of life of CMSP patients. Allpsychological scorings were improved secondary to improvedpain scores but improved kinesiophobia is the significantpredictor for alleviation of depression