Computer Science & IT Research Journal最新文献

{"title":"NEW INSIGHTS ON SURVIVABILITY IN MULTIPLE LINK AND NODE FAILURES OF OPTICAL NETWORKS","authors":"None Henry Naiho, None Seleman Ngwira","doi":"10.51594/csitrj.v4i1.591","DOIUrl":"https://doi.org/10.51594/csitrj.v4i1.591","url":null,"abstract":"Survivability becomes increasingly critical in managing high-speed networks as data traffic continues to grow in both size and importance. In addition, the impact of failures is exacerbated by the higher data rates available in optical networks. The purpose of this study is to optimize the survivability approach to address the problem of capacity efficiency and fast recovery time of multiple link and node failures in one technique. Most research works done had only addressed either of these constraints in a single failure. This research intends to develop and implement a new integrated approach called Multi-Suv system model to address multiple failures in links and nodes of an optical network. Experiments was conducted to demonstrate the ability of the system model to identify the nature of failure based on the intensity of importance as defined in the fundamental scale of absolute numbers (Saaty, 1980); thus, inform on the path of restoration or protection to address the link and node failures. Following experimental simulations done on the model, the results shows that there are benefits amongst are reduced network resource usage, speedy recovery time, guaranteed availability and quality of reliability. The socio-economic value of this research will reduce the CAPEX capital investment of network infrastructures and facilities and OPEX the operational costs of service delivery of the ICT industry thereby increasing the profitability of the network and service providers. 
 Keywords: WDM (Wavelength Division Multiplex), OXADM (Optical cross Add & Drop Multiplexer), Intelli-MUX (Intelligent Multiplexer), DeMux (De-multiplexer, OPSS (Optical Switching System), OPXC (Optical Cross Connect), CAPEX (Capital expenditures), OPEX (Operations expenditures, OPM (Opinion Performance Matrix).","PeriodicalId":257508,"journal":{"name":"Computer Science & IT Research Journal","volume":"22 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136135421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A REVIEW OF POST-COVID TELECOMMUNICATION INVESTMENT TRENDS: IMPACTS ON INFRASTRUCTURE DEVELOPMENT","authors":"None Chibuike Daraojimba, None Adebowale Daniel Bakare, None Joy Otibhor Olurin, None Kehinde Mobolaji Abioye, None Moses Ikechukwu Obinyeluaku, None Donald Obinna Daraojimba","doi":"10.51594/csitrj.v4i1.577","DOIUrl":"https://doi.org/10.51594/csitrj.v4i1.577","url":null,"abstract":"The post-COVID era has catalysed a transformative phase in the telecommunication sector, with dramatic shifts in both consumer behaviour and enterprise needs. This study offers a synthesized review of investment trends in telecommunications following the pandemic, detailing their implications on infrastructure development. The initial pandemic response saw an exponential rise in the demand for robust connectivity, necessitating rapid infrastructural adjustments to cater to remote work, online education, and digital healthcare. This paper identifies significant post-COVID investment trends in telecommunications, which include an augmented drive for broadband and fibre-optic expansion, an accelerated rollout of 5G networks, heightened investments in cloud services and data centres, and a notable surge in mergers and acquisitions. This investment influx has palpably advanced infrastructure development rates, with a focus on enhancing capacity, speed, reliability, and geographical reach. However, the ramifications of this swift expansion are multifaceted. On the socio-economic front, there has been substantial job creation, technology democratization, and improved accessibility, especially in historically underserved areas. In parallel, the environmental footprint of this growth is also scrutinized, shedding light on increased energy consumption, challenges in electronic waste management, and resource utilization. From a business perspective, while there's enhanced market competition and improved service quality, companies also grapple with new challenges in customer retention, experience management, and competitive differentiation. Furthermore, this paper elucidates evolving policy landscapes, marked by modified telecom regulations, incentivization strategies, and measures to ensure equitable access. In conclusion, the post-pandemic telecommunication investment trends have undeniably fast-tracked infrastructure development, but with nuanced implications for society, environment, business, and policy. The forward trajectory, though promising, requires a harmonized effort from stakeholders to ensure sustainable and inclusive growth. 
 Keywords: Telecommunication, Infrastructure, Investment, Government, Post-Covid, Policy.","PeriodicalId":257508,"journal":{"name":"Computer Science & IT Research Journal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135095451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE","authors":"A. Halliru, G. Wajiga, Y. M. Malgwi, Abba Hamman Maidabara","doi":"10.51594/csitrj.v3i1.290","DOIUrl":"https://doi.org/10.51594/csitrj.v3i1.290","url":null,"abstract":"The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. \u0000Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining.","PeriodicalId":257508,"journal":{"name":"Computer Science & IT Research Journal","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115977416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE PREDICTION OF HEPATITIS B VIRUS (HBV) USING ARTIFICIAL NEURAL NETWORK (ANN) AND GENETIC ALGORITHM (GA)","authors":"Douglas Ibrahim, A. S. Ahmadu, Y. M. Malgwi, Bamanga Mahmud Ahmad","doi":"10.51594/csitrj.v2i1.275","DOIUrl":"https://doi.org/10.51594/csitrj.v2i1.275","url":null,"abstract":"The hepatitis B virus causes a liver infection called hepatitis B (HBV). It might be severe and go away on its own. Some kinds, however, can be persistent, leading to cirrhosis and liver cancer. HBV can be transmitted to others without the individual being aware of it; some persons have no symptoms, while others only have the first infection, which later resolves. Others develop a chronic illness as a result of their condition. In chronic cases, the virus attacks the liver for an extended period of time without being detected, causing irreparable liver damage. The manual approach has a high number of errors due to human decision-making, and visual screening is time-consuming, tiresome, and costly in terms of manpower. To predict the occurrence of Hepatitis virus (HBV), this research project thesis suggested an algorithm; Artificial Neural Network (ANN), and genetic algorithm (GA). To develop, evaluate and validate the performance of the model developed using ANN. Medical records of nine hundred patients were collected in the Northern Senatorial District (Mubi South), Central Senatorial District (Hong), and Southern Senatorial District (Ganye) regions of Adamawa state, Nigeria. Three hundred (300) patient records were collected from each general hospital, for a total of 900 patient records. The success of the proposed technique is demonstrated when ANN is paired with GA, Accuracy (66.30%), Specificity (66.33%), and Sensitivity (77.53%) were discovered. In this study, hepatitis B virus (HBV) was predicted using Artificial Neural Network (ANN) classifier and Genetic algorithm optimization tool were used to select the features that are responsible for hepatitis B virus (Sex, Loss of Appetite, Nausea and vomiting, Yellowish skin and eye, Stomach pain, Pain in muscles and joint). The prediction was found to have acceptable performance measures which will reduce future incidence of the outbreak and aid timely response of medical experts. \u0000Keywords: Hepatitis B Virus (HBV), Prediction, Features, Classification.","PeriodicalId":257508,"journal":{"name":"Computer Science & IT Research Journal","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121543820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EXPERT SYSTEM FOR DIAGNOSIS OF MALARIA AND TYPHOID","authors":"Abba Hamman Maidabara, A. S. Ahmadu, Y. M. Malgwi, Douglas Ibrahim","doi":"10.51594/csitrj.v2i1.274","DOIUrl":"https://doi.org/10.51594/csitrj.v2i1.274","url":null,"abstract":"An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis.\u0000Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malaria","PeriodicalId":257508,"journal":{"name":"Computer Science & IT Research Journal","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124357996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}