Zambia ICT Journal最新文献

{"title":"A Privacy-Preserving Scheme for Medical Diagnosis Records Based on Encrypted Image Steganography","authors":"Kaku Lishomwa, Aaron Zimba","doi":"10.33260/zictjournal.v7i1.151","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.151","url":null,"abstract":"Healthcare diagnosis records are essential as they establish perpetual reports on the health of patients. These records are in nature meant to be kept confidential. It is for this reason that medical data should be secured before transmission. Steganography and cryptography are old concepts used to secure communications. Steganography is hiding data into a carrier, for instance, images, videos, texts, and files. It seeks to hide the existence of communications. Cryptography is communication in deciphering secret writings or ciphers. In our privacy-preserving scheme for healthcare records, a hybrid approach is used to ensure the maximum multi-level security of medical records before transmission. The proposed system utilizes AES-128 which is a symmetric key encryption algorithm to scramble the data. As compared to asymmetric key encryption, symmetric key encryption is faster as only one key is used for both encryption and decryption. To resolve the problem of key distribution that plagues symmetric cryptosystems, the proposed system makes use of the Diffie-Hellman key exchange algorithm for key agreement. In this way, the encryption essentials are securely exchanged before the actual transmission of the date. To ensure efficient and optimal steganography throughput, the Least significant bit (LSB) which involves the hiding of information in the most repetitive bits of each pixel of an image was implemented for steganography. The technique was chosen because the level of distortion made to the image is low hence the image quality change is minimal. As such, the resultant robust system, is highly impeccable, utilises efficient compression, and has improved capacity which are all desirable features for securing medical health records.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121125232","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 Supervised Machine Learning Ransomware Host-Based Detection Framework","authors":"Yotam Mkandawire, Aaron Zimba","doi":"10.33260/zictjournal.v7i1.132","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.132","url":null,"abstract":"Today, the term ransomware is frequently used in cybercrime headlines, its consequences have been on the rise leaving a trail of terrible losses in its wake. Both people and businesses have been victimized by ransomware, costing the victims millions of dollars in ransom payments. In addition, victims who were unable to pay the ransom or decrypt the data experienced data losses. This study uses dynamic malware analysis artifacts and supervised machine learning to detect ransomware at the host level. It takes on a thorough examination of the operational specifics of ransomware and suggests a supervised machine-learning approach to detection using various ransomware features derived from dynamic malware analysis. According to the findings, a Logistic Regression algorithm model with a 97.7% accuracy score offers a 99% success rate in ransomware detection. This demonstrates how well machine learning and dynamic malware analysis work together to detect ransomware activity at the host level. Systems security administrators can mitigate security risks by using this method.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134236539","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":"Assessing Automated Flood Disaster Alert Systems in Zambia: Case of Mbeta Island in Sioma, Western Province","authors":"Clifford Siatala, J. Mbale","doi":"10.33260/zictjournal.v7i1.118","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.118","url":null,"abstract":"Globally, as the advancement and mainstreaming of Technology become more perversive, ICTs have become agents of change transforming the way we conduct and perceive human activities to sustain and promote the quality of life. It is also no surprise that today’s technology pros control everything from telephone system to software compliance and disaster recovery that seem to become sole candidates for change. For instance, todays ICTs have been integrated in modern lifestyles and has become perversive in everything from telephone networking, automated security systems, software compliance and backups to disaster recovery platforms and Internet of Things. Additionally, one may argue that it is no longer feasible or rapidly becoming obsolete to solely rely on manual systems to predict potential threats such as natural and man-made disasters. Thus implementing early warning systems, communities play an important role in leveraging ICTs potential to generate warning hazards through automated systems that escalates emergency notifications to rescue and mitigation teams. Using a positivist approach with methodological triangulation at data collection and analysis, this study assessed automated flood disaster alert systems in Zambia with specific reference to Mbeta Island in Sioma. The study was informed by Unified Theory of Acceptance and Use of Technology (UTAUT) theoretical framework with questionnaires and interviews as measurement instruments to assess factors that led to the adoption of the alert system installed in Mbeta Island. Study findings showed varying impact of independent variables: Effort expectancy 6.2%, Performance expectancy 11.8%, Facilitating conditions 4.4% and Social influences 2.6% respectively. The total impact showed there were factors left out in the implementation of the alert system and that there is a need to adopt a new and robust disaster mitigation system that would allow for integration of community needs and everyday challenges in relation to natural and man-made disasters.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124968631","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":"Current Trends in Machine-Based Predictive Analysis in Agriculture for Better Crop Management - A Systematic Review","authors":"Mulima Chibuye, J. Phiri","doi":"10.33260/zictjournal.v7i1.147","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.147","url":null,"abstract":"The use of Artificial Intelligence in agriculture is a novel approach that promises many benefits. Notable is the emphasis by nations of the world to end hunger by 2030 as enshrined in Sustainable Development Goal number 2[1]. To end world hunger, the fundamental ways of doing things in and around the agricultural space will have to change by adopting much more sustainable models and relooking at the supply chain system with the space. For example, it is noted that more food goes to waste through spoilage than is required to feed all the hungry on earth. While in other parts of the globe, the food supply would be sufficient were it not for the stock that spoils due to pests and diseases. It the goal of this paper to provide a possible solution for the second scenario on spoilage due to pests and diseases by adopting Artificial Intelligence approaches such as Machine Learning and tweaking existing methods by improving the overall prediction score. We provide areas of interest that may be considered and show that further research in the subject may yield positive results in the field of Predictive Analysis as concerns the field of agriculture. A Systematic Review is done on over 20 pieces of literature around the field of Predictive analysis and notable gaps are highlighted while areas of possible improvement are also indicated. It is then against this backdrop that the highlighted areas of improvement may later be tested in subsequent work.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125364701","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":"Detecting Hate Speech and Offensive Language using Machine Learning in Published Online Content","authors":"C. Sinyangwe, D. Kunda, William Phiri Abwino","doi":"10.33260/zictjournal.v7i1.143","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.143","url":null,"abstract":"Businesses are more concerned than ever about hate speech content as most brand communication and advertising move online. Different organisations may be incharge of their products and services but they do not have complete control over their content posted online via their website and social media channels, they have no control over what online users post or comment about their brand. As a result, it became imperative in our study to develop a model that will identify hate speechand, offensive language and detect cyber offence in online published content using machine learning. This study employed an experimental design to develop a detection model for determining which agile methodologies were preferred as a suitable development methodology. Deep learning and HateSonar was used to detect hate speech and offensive language in posted content. This study used data from Twitter and Facebook to detect hate speech. The text was classified as either hate speech, offensive language, or both. During the reconnaissance phase, the combined data (structured and unstructured) was obtained from kaggle.com. The combined data was stored in the database as raw data. This revealed that hate speech and offensive language exist everywhere in the world, and the trend of the vices is on the rise. Using machine learning, the researchers successfully developed a model for detecting offensive language and hate speech on online social media platforms. The labelling in the model makes it simple to categorise data in a meaningful and readable manner. The study establishes that in fore model to detect hate speech and offensive language on online social media platforms, the data set must be categorised and presented in statistical form after running the model; the count indicates the total number of data sets imported. The mean for each category, as well as the standard deviation and the minimum and maximum number of tweets in each category, are also displayed. The study established that preventing online platform abuse in Zambia requires a comprehensive approach that involves government law, responsible platform policies and practices, as well as individual responsibility and accountability. In accordance with this goal, the research was effective in developing the detection model. To guarantee that the model was completely functional, it was trained on the English dataset before being applied to the local language dataset. This was because of the fact that training deep learning models with local datasets can present a number of challenges, such as limited, biased data, data privacy, resource requirements, and model maintenance. However, the efficacy of these systems varies, and there have been concerns raised about the inherent biases and limitations of automatic moderation techniques. The study recommends that future studies consider other sources of information such as Facebook, WhatsApp, Instagram, and other social media platforms, as well as consider harvesting local","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125610876","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":"Towards Election Forecasting Using Sentiment Analysis: The Zambia General Elections 2021","authors":"Yasin Musa Ayami, Mayumbo Nyirenda","doi":"10.33260/zictjournal.v7i1.148","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.148","url":null,"abstract":"Forecasting of election results is one of the key activities prior to elections. In Zambia, like many other countries, opinion polls have been used to predict the outcome of elections since 1999. During the run up to the 2021 general elections, two opinion polls were conducted. One poll suggested that HH would emerge victorious whilst the other predicted that ECL would emerge victorious. Actual results announced on the 16th of August 2021 by the Electoral Commission of Zambia (ECZ) had HH obtaining 59.02% of the votes. The variance in the two opinion polls leaves room for alternative approaches to predicting election results. This study proposes sentiment analysis as part of the initial stage to building an alternative solution to predicting the outcome of an election. The study analysed sentiments shared on social media during the build up to the August 2021 general elections. A total of 3,519 tweets were scrapped from Twitter and sentiment analysis was performed on the tweets. Topic modeling was subsequently also performed on the tweets using BERTopic. The findings of the study reveal that as election day drew closer, there was an exponential increase in the number that were posted on a daily basis. Some of the topics included voter engagement and education, the shutdown of the internet and the election day.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131731639","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":"Challenges of Crowdfunding (Village Banking) in Zambia: Solutions and Opportunities","authors":"Prudence Kalunga, Victor Neene, Bob Jere, Mwiza Phiri, C. Chembe","doi":"10.33260/zictjournal.v7i1.131","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.131","url":null,"abstract":"Crowdfunding is a new phenomenon that has piqued the curiosity of academics and practitioners alike, owing to its potential as a source of alternative finance. It is considered a mechanism with significant potential for expanding access to finance for entrepreneurs in developing economies. One such platform in Zambia is known as “Village Banking”. Village banking has been discovered to be the most profitable platform in Zambia, outperforming even traditional risk-free platforms such as micro-financing. Village banking has enabled business growth, idea realization, and has generally improved the lives of many participants. The risks associated with the village banking system, on the other hand, have been increasing. As the popularity of crowdfunding has increased, so have the challenges and opportunities. In this paper we employ a systematic literature review that defines crowdfunding, its properties, discusses the challenges, opportunities of Crowdfunding specifically the Village Banking platform. In addition, we conducted a survey to determine the main challenges faced by Village Banking participants in Zambia. The results of the survey identified the following primary challenges; online infrastructure, trust, monitoring of payments, tracking of collections, and approval of payments. Based on literature reviewed and survey conducted, we proposed the use of blockchain technology to address some of the challenges identified.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572914","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":"Detecting Covid-19 and Other Lung Diseases with Deep Learning","authors":"Yamikani Tembo, George Mweshi","doi":"10.33260/zictjournal.v7i1.135","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.135","url":null,"abstract":"Since December 2019, Covid-19, also known as severe acute respiratory syndrome (SARSCOV-2), has infected about 531 million individuals worldwide causing devastating consequences. Scientists, health professionals, and radiologists have all invested huge amounts of time, effort and resources in developing faster and accurate methods for detecting the virus as it spreads all over the world. The current gold standards for detecting covid-19 such as RT-PCR, POC, Droplet-based digital PCR (dPCR) and Immunoassays are not only expensive for low-income countries but also require substantial human expert knowledge which in turn makes them time-consuming and susceptible to manipulation. The study presented in this paper aims to address these issues by proposing a model to automatically detect COVID-19 in chest X-rays using Deep Learning techniques. Specifically, the model uses convolutional neural networks (CNNs), a deep learning technique, which has been shown to produced very good results when applied to medical image diagnosis problems. Our proposed model will therefore not only provide a cheaper, faster and accurate method of detecting COVID-19 but will also provide an easy to use web and mobile platform for non-experts. While current detection techniques such the RT-PCR are still considered as the most effective, our CNN-based model was also able to produce good results when applied to the COVID-19 radiography dataset. For instance, the model was able to detect COVID-19 from X-ray images with an accuracy rate of 90 percent. Furthermore, the model was also able to automatically detect other respiratory diseases such as viral pneumonia and lung opacity with an accuracy rate of over 90 percent. Given the potential of the automated detection of COVID-19 established in this study, future work will extend this work by incorporating automated search techniques such as genetic algorithms in the fine tuning of the model’s parameters in order to obtain an even higher accuracy rate.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092056","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":"Synthetic Aperture Radar and Optical Sensor Techniques Using Google Earth Engine For Flood Monitoring and Damage Assessment – A Case Study of Mumbwa District, Zambia","authors":"Christopher Shilengwe, P. Nyimbili, Robert Msendo, F. Banda, Wallace Mukupa, T. Erden","doi":"10.33260/zictjournal.v7i1.122","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.122","url":null,"abstract":"Radar and Optical based satellite sensors were used in the study of the Mumbwa flood of December 2020. The Synthetic Aperture Radar (SAR) based Sentinel-1B was processed in Google Earth Engine (GEE) and utilized to generate an image mosaic from December 2020 to May 2021 to delineate flood extent. A local water histogram threshold change detection approach by image ratio was utilized to determine the flood extent with an intensity value of 1.26 dB as it fitted the study area uniquely as opposed to the global value of 1.25 dB. After extracting the initial flood water extent, it was necessary to filter out regions which inundated during the flood period. This was carried out using the following datasets and parameters: The HydroSHEDS Digital Elevation Model (DEM) was used to filter out regions with a slope value of greater than 7% and the Global Surface Water Layer was used to clip out regions with existing permanent surface water. Once the flooded areas were identified, the Optical based Sentinel-2 was used in the production of a Land Use Land Cover (LULC) Map for August 2020 in order to superimpose the flooded areas with existing land features over the study area. The map also under went pre and post processing in GEE using the Random Forest Classification Algorithm that achieved an Overall Accuracy and Kappa Coefficient of 0.957 and 0.91519 respectively. Thereafter the flood analysis and damage assessment were carried out. The quantitative damages to Landcover were found to be: Wetland 6,338.97 Ha (33.27%), Shrubland 5,117.75 Ha (26.89%), Biochar Soil 3,660.47 Ha (19.21%), Trees 3,466.37 Ha (18.19%), Bare soil 273.47 Ha (1.44%), Crop Fields 190.69 Ha (1%) and Built-Up 4.13 Ha (0.02%). Therefore the use of SAR by local histogram threshold approach with Optical datasets for LULC map production proved successful in the study of flood damage.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134466271","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":"An Arduino-Based Smart Digital Flushable Toilet for Efficient Water Management And Enhanced Hygiene","authors":"Winston Kabaso, Aaron Zimba","doi":"10.33260/zictjournal.v7i1.136","DOIUrl":"https://doi.org/10.33260/zictjournal.v7i1.136","url":null,"abstract":"Despite flushable toilet invention being helpful to humans, especially in towns and cities, flushing waste has become very expensive both in household and public places coupled with health risks of contracting some infectious diseases like streptococcus, diarrhea, etc, due to its use. These are primarily spread in public toilets such as hospitals, schools, bus stations, etc. Global warming and climate change have caused water management to become an issue that requires a concerted effort from all users, if not well managed at household and public levels, the monthly cost becomes high as some people tend to flush urine with the same amount of water as if it were stool by emptying the entire cistern into the bowl which is a waste of water hence pausing an economic challenge at both household and public places. Many people do not sanitize the toilet seat before use which if not well handled may harbor germs that may infect users. Therefore, the developed system ensures that there is automated flushing of waste with a distinct amount of water for urine and stool. The developed system also automatically sanitizes the toilet seat before someone sits on it hence reducing infectious diseases like covid-19. The system was developed using technologies and frameworks like Arduino Microcontroller which controls how the various toilet parts operate automatedly and C programming language was used for code.","PeriodicalId":206279,"journal":{"name":"Zambia ICT Journal","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319305","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}