Department of Computer Science;

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Browsing Department of Computer Science; by Title

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    A 3D LASER SCANNING DEVICE FOR OBJECT RESTORATION USING POINT CLOUD REGISTRATION
    (Lead City University, 2022-12) Ahmed Abba Sheik
    3D scanning of environments and the objects have a lot of practical uses. During the last decade increased performance and reduced cost has made them more accessible to larger consumer groups. The price point is still however high, where popular scanners are in the price range of 300,000 NGN-500,000 NGN. The objective of this thesis is to investigate the current 3D scanners in the market, considering both time-of-flight and triangulation in terms of accuracy and limitations and compare there results to build a more cost effective model of the 3D scanner at the end of the thesis. For validation purposes the constructed 3d scanner will be put through tests to measure its accuracy and ability to create realistic representations of its environment. The constructed model produced significantly less accurate results and scanning time was much longer compared to a popular competitor. This was mainly due to the cheaper camera sensor used for the model and not the mechanical construction itself. There are however many applications where higher accuracy is not essential and with some modifications, a low cost solution could have many potential use cases, especially since it only costs 1% of the compared product. Keywords: Triangulation, 3D scanner, Time-of-flight, Laser scanner Word count 185
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    A Dual-Mode Radio-Frequency Identification and Facial Recognition System for Attendance Capturing
    (Lead City University, 2023-12) Yinka John ADEGOKE
    In today's rapidly evolving technological landscape, efficient and secure attendance tracking systems are essential for various organizations. This study introduces a novel solution that combines Radio-Frequency Identification (RFID) and Facial Recognition technologies to create a robust attendance management system. By leveraging the capabilities of both hardware and software components, this system offers a seamless and accurate approach to recording and managing attendance data. The hardware component of the system utilizes Arduino microcontrollers and RFID modules to provide individual identification through RFID cards or tags. Each user is assigned a unique RFID card that triggers the RFID module to record the attendance information. Simultaneously, the system captures facial images using a camera module for facial recognition. A Python program processes the data using Open CV, associating it with the respective user's profile and initiates the facial recognition process. The facial recognition system identifies users by comparing the captured facial features with the pre-stored templates in the database. The system offers several advantages, including high accuracy in attendance recording, enhanced security, and rapid processing of data. Moreover, the combined approach reduces the time spent on proxy attendance, ensuring the integrity of attendance records, and creating options for attendance. The system also provides real-time attendance tracking and generates comprehensive reports for administrative purposes. This research presents a step- by-step implementation guide for setting up the RFID and Facial Recognition Attendance System using Arduino and Python, making it accessible for educational institutions, businesses, and organizations looking to streamline attendance management. The system's effectiveness is demonstrated through extensive testing, highlighting its reliability and robustness. The system represents a cutting-edge solution for modern attendance management needs. By harnessing the capabilities of the technologies adopted, this system offers a secure, accurate, and efficient approach to attendance tracking, paving the way for improved organizational efficiency and data integrity. Keywords: Python , Facial Recognition , Arduino, A Dual-Mode Radio, Frequency Identification, System, Attendance Capturing Word Count: 299
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    A Hybrid Swarm Intelligence Convolution Neural Network for Object Detection and Tracking
    (Lead City University, 2023-12) Afiss Emiola KAREEM
    In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Despite significant efforts in object detection and tracking, an efficient method which provides high computational efficiency has not been developed. Hence, in this work a Hybrid Particle Swarm Optimization Convolution Neural Network (CNN-HPSO) technique was developed to improve computational efficiency in object detection and tracking. The video datasets (MP4 and AVi video formats) used in this work were obtained from a conventional online database and on a real-time basis from YouTube. Multiple frames sampled from the video clips were pre-processed and then segmented. An Enhanced Particle Swarm Optimization (HPSO) was formulated from standard PSO and was applied to Convolution Neural Network (CNN). CNN- HPSO technique was used for edge detection and extraction of the boundary of the image and the object tracking was finally carried out. The work was implemented using MatLab R2016 software. The average results of CNN-HPSO, CNN-PSO and CNN on the videos with MP4 format yielded processing time, accuracy, precision, FPR, sensitivity and specificity of 165.89s, 97.08%, 98.41%, 7.75%, 97.82%, and 92.25%; 179.52s, 94.25%, 96.99%, 10.37%, 95.23% and 89.62%; and 189.19s, 89.95%, 93.64%, 15.56%, 91.95.33% and 84.44% respectively. For the videos in AVi format, CNN-HPSO, CNN-PSO and CNN produced similar average results with processing time, accuracy, precision, FPR, sensitivity, and specificity of 185.09s, 96.62%, 98.23%, 7.80%, 97.34% and 92.19%; 198.24s, 94.83%, 97.62%, 8.56%, 95.46% and 91.43%; and 216.59s, 91.30%, 93.98%, 15.09%, 93.67% and 84.91% respectively. In this research, a CNN-HPSO with associated high computational efficiency was developed. The developed technique can be used for solving other related optimization problems. Keywords: Deep Learning Algorithms, Computer Vision, Moving Objects, Video Frame, Object Segmentation Word Count: 293
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    A Mathematical Morphological Deep Neural Network for the Classification of Periapical Radiographs in the Diagnosis and Treatment of Dental Diseases
    (Lead City University, 2022-12) Grace Lawumi TAM-NURSEMAN
    One of the best ways to diagnose a disease in medical practice objectively is through medical imaging. The importance of medical imaging cannot be overemphasized. In dentistry, dentists often use radiographs, especially in finding hidden dental structure, bone loss, malignant or benign masses, and cavities that cannot be examined during a visual examination. The use of dental radiographs also helps dentists to detect hidden dental diseases early. This study is a continuation of previous work which bothered on the development of an expert system for the diagnosis and prognosis of 20 Common dental diseases using Bayesian network. The work was developed using several symptoms associated with dental disease for diagnosing dental diseases (D1- D20). The study was limited to symptomatic diagnosis which however has some obvious gaps such as the uncertainty in the reasoning associated with Bayes rule, the use of Pain as a parameter among others that were filled through the use of deep learning tools on dental periapical radiographs through an improved model. The improved model was developed integrating mathematical morphology (MM) operations (dilation, erosion, opening and closing) in the convolution layer of CNN, for data preprocessing and quality feature extraction. With its high sense of intelligence (artificial) obtained during training, the system receives dental images and analyses them automatically for various clinical findings with which 6 dental disease problems were solved. With an achieved accuracy of 99.78%, it can be established that this system can be used in dental clinics with high confidence giving very little or no-error-diagnosis. To make this system more scalable and robust, more dental diseases should nbe added through other MM based theory like lattice, topology and random functions other than set theory-based MM used in this study. Keywords: Mathematical Morphology (MM), Dilation, Erosion, Opening, Closing, Convolutional Neural Network (CNN) Word Count: 285
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    A Web Based Chatbot for Mental Health Support
    (Lead City University, Ibadan, 2023-12) Samuel Ejomafuvwe LUCKY
    Despite the significance attributed to mental health, a considerable number of individuals have difficulties in accessing prompt and tailored mental health interventions. This predicament can be attributed to various factors, including societal stigmatisation, limited availability of resources, and residing in geographically isolated areas. This study addresses the persistent challenge of providing timely and individualized mental health treatment through the development of a web-based chatbot for personalized therapy. The study comprises the utilisation of a dataset containing frequently asked questions (FAQs) related to mental health. Preprocessing techniques, including lemmatization, lowercasing, and duplication removal, are employed in order to prepare the data for analysis. The machine learning model, which utilises neural networks, undergoes training and has a negative association between epochs and loss magnitude, suggesting enhanced performance as the training progresses. The findings indicated that the developed chatbot demonstrated a high level of proficiency in delivering personalised mental health care that is relevant to the individual, providing fast responses, and offering appropriate recommendations for therapy. Additionally, the user feedback received during the performance evaluation highlights a high level of satisfaction and a strong inclination to utilise the chatbot again in the future. The study highlights the potential of chatbots, particularly those based on LSTM architecture in effectively addressing mental health issues and enhancing the availability of resources. The study therefore recommends that continuous improvement refining and enhancing the chatbot's capabilities by regularly updating the chatbot's knowledge base, therapy recommendations, and conversational abilities to ensure it remains relevant and effective. Keywords: Epochs, Frequently asked questions (FAQs), Lemmatization, Lowercasing, LSTM architecture, Machine learning model, Mental health, Personalized therapy Word Count: 247 Words
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    Academic Journal Plagiarism Checker Using Tri-Gram Algorithm and KNN Algorithm
    (Lead City University, 2022-12) Iyanuoluwa Modupeore FATOKI
    Academic journal plagiarism checker using data mining is aimed at checking for plagiarism in journals submitted by students, in which the research title has a unique name since two journals in the database are not allowed to have similar titles. The main goal of this thesis is to develop a journal plagiarism checker and provide an interface that will be used for journal submission and check for plagiarism. The objectives in achieving the set goal are: Designing of the document repository, design of the design of the plagiarism checker algorithm and the development of the whole system with HTML, CSS, PHP, JavaScript, Xamp Server. The system is targeted to helping students take their research seriously and also to reduce the rate at which students plagiarize in their research work and to also help students with the selection of research topic that has not been done by someone else before so as to prevent plagiarism. The journal plagiarism checker makes use of clustering techniques in data mining and KNN algorithm and Trigram algorithm is deployed for the implementation of the system. The system will give accurate plagiarism report and it will take less time to complete its execution. The cost of implementing the system is minimal compared to the cost of other plagiarism tools available online. The system is deployed into the Department of Computer Science in Lead City University. Keywords: Plagiarism, KNN Algorithm, Trigram Algorithm, Journal. Word Count: 232
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    Advanced Surveillance Technology Multicast Using Optical Wireless Transceiver in Smart Environment
    (Lead City University, Ibadan, 2023-12) Israel Oluwagbejamija FAKUNLE
    Security practice is crucial peaceful living. In the old times, before the advancement of technology, security was a major concern due to invasions, robbery, and wars. According to history, security personnel in those days known as vigilante also served as police. The security responsibilities then require 100% human effort, having to go over an assign geographical area, restlessly and sleeplessly, to secure lives and properties. But today with technological advancements, people are able to live in security without the need for protection. The advancement in technology as relieved human a whole lot of security threats and stress. This study aims to develop a real-time surveillance system that utilizes multicast technology to prevent and detect crime in an enclosed geographical location. The objective is to empower residents to work together and contribute to the security of their environment, lives, and properties. Real-time surveillance multicast is faced with numerous challenges, such as; lags / interruption in transmission, due to error from the framework or internet connections, high internet data consumption, due to enormous data transmission and limited number of users allowed. A Close-Circuit Television system will be designed using an analogue camera and digital video recorder with a hard drive for data capturing and storage allowing decentralization of the system using a wireless video transceiver through integration. Overall, this study aims to develop a surveillance system that empowers residents to work together and contribute to the security of their community. The system will leverage advanced technologies such as wireless video transceivers and multicast technology to improve the efficiency and effectiveness of surveillance. Keywords: Technological Advancement, Security Threats, need for protection, real-time surveillance, multicast, lags in transmission, empower residents. Word Count: 260
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    An Improved Call Quality for Call Drop Minimization during Handover in Mobile Communication
    (Lead City University, Ibadan, 2023-12) Temilola Adedamola JOHN-DEWOLE
    Mobile devices have become essential and significant aspects of everyone's life in the modern technology. Call drops are significant problems for telecommunications network providers. Users’ call quality being negatively affected by mobile call drops, have also lowers revenue generation for telecom service providers. From the literature, the call quality for a group of calls could be predicted based on combination of calls’ successful factors. This study aims at developing an improved call quality for call drop minimization during handover in mobile communication. In order to address the call drop, a model of neural network to enhance call performance and effectiveness was created. Top-performing Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) models were selected and their predictions were combined using a weighted average ensemble approach. The Ensemble Models machine learning approach was employed using Python software. The features used were signal strength, call drop rate, data usage, call types, congestion level, call setup success rate and traffic control congestion rate. The study utilized a dataset with a total of 3000 data points across 30 cell towers and with each cell running for 5 minutes. The performance is evaluated using accuracy, precision, recall, f-score measures and auc-roc. Results of the research gave an accuracy of 97.18 %, 96.64 % precision, 96.58 % recall, 96.11 % f-score measures and auc-roc of 98.79 % for call drop quality. This strongly correlate with existing results of 90 % accuracy, 93 % precision, 92 % recall, 90 % f-score measures but no auc- roc. Another research showed overall accuracy of 95 %. It is therefore recommended that telecommunications companies should implement deep learning techniques on cellular network data to reduce and fix call drops so that consumers will have a higher call quality in the future; providing continuous communications. Keywords: call drop, call quality, call setup success rate, LSTM, CNN, ensemble models, deep learning, telecommunication providers. Word Count: 295
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    An Optimized Low-Level Interaction Glastopf Honeypot for Accurate Detection of Fake Honeypot using OMNET++ Simulation
    (Lead City University, 2023-12) Abimbola Basiru OWOLABI
    Securing cloud-based information has become the most critical aspect in computing, owing to different methods adopted by attackers to steal vital information. Most system developers focus their information security on defensive mechanisms against attackers, this has proven to be inactive as attackers continuously explore ways to gain unauthorized access to cyberspace information, which has necessitated more effective, robust, and efficient models to mitigate threats posed by Cyber criminals. Hence, the design of an optimized web-based low-interaction Glastopf honeypot for the accurate gathering of Attackers' intelligence information and detection of fake honeypot systems using the OMNET++ Simulation tool. This study aims to assess the effectiveness of the Glastopf honeypot in collecting relevant intelligence on attackers, detecting fake honeypot systems, analyses the honeypot's ability to capture and record attackers' actions, including their exploitation methods, tools used, and payloads deployed. It also evaluates the honeypot's ability to provide valuable insights into attackers' motivations, intentions, and potential targets. An extensive experiment was conducted by setting up a virtual system with OMNET++ simulation running on Ubuntu web-server on the back end while on the front end was windows operating system where Glastopf honeypot was configured in a controlled environment. The study injects Hornet 40 data sets of attacks collected from six different cloud servers into the server to test Glastopf honeypot. Multiple attack scenarios were simulated, involving various types of attackers and attack vectors. The honeypot's logs, network traffic captures, and other relevant data are collected and analyzed using automated techniques. The results of the experiment provide insights into the Glastopf honeypot’s effectiveness in gathering intelligence information and make Glastopf honeypot a good cyber security tool, but would perform better when deployed with IDS and firewalls, thereby recommended for organizations but would not be suitable for individuals due to the installation technicality involved. Keywords: Deception Technology, Honeypot, IDS, OMNET++, Glastopf, Web Application Word Count: 298 words
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    Applicability of Machine Learning Algorithms to Analyze Despondency Comments on Social Media Using Analytical Hierarchical Process (AHP)
    (Lead City University, 2022-12) Damilola Alaba HALLY
    Depression is a serious mental illness that affects an individual’s professional and personal life. With the development of internet usage people have started to share their experiences and challenges with mental disorder through online platforms. Social media platforms come close to being a true digitization of the human social experience. In many cases people would prefer to express themselves online rather than offline especially in completed suicide attempts around the world. This thesis objectives are to extract despondency indicative social media posts, categorize these posts and then apply an integration of machine learning techniques to generate markers in identifying depressive comments in social media. This will be examined using five algorithms; Support Vector Machine, Logistic Regression, K-Nearest Neighbor, Naves Bayes and Linear Regression. The Analytical hierarchical Performance was used to determine the best algorithm to detect depression in terms of performance metrics. This process identifies users who are at risk of depression to initiate quick intervention. The result of this thesis shows that the support Vector machine has the highest performance metrics. This signifies that the support vector Machine is the best algorithm to apply for the extraction of Despondency symptoms in social media, this will also determine the best machine learning Algorithm that has the best performance and accuracy in detecting despondency symptoms on social media. It is recommended based on the conclusions of this thesis that the support vector machine Algorithm be used in institutions social media platforms to adequately monitor despondency in individuals Keywords: Despondency, Machine learning Algorithm, Analytical Hierarchical Performance, Support Vector Machine, Logistic Regression, Word count: 246.
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    Automated Customer Support System (Chatbot) to Enhance Web-based Financial Application Services using Artificial Intelligence
    (Lead City University, 2023-12) Roqib Akintunde AKINYEMI
    Customer support is perhaps one of the main aspects of the user experience for online services. However, with the rise of natural language processing techniques, the industry is looking at automated chatbot solutions to provide quality services to an ever-growing user base. In view of this, the chatbot was developed using Artificial Intelligence Markup Language (AIML) java interpreter library Program AB (an experimental platform for the development of new features and serves as the reference implementation) which helps match input and output predefined in the AIML file. AIML was used to preprocess and train the bot using ready-made AIML file for Frequently Asked Questions. Also, vaadin was used to build a web user interface to interact with the trained AIML bot. Finally, a google script was written to translate from any language to English for the bot to understand and send the response in the preferred language of the user. Findings showed that the response time of the bot is dependent of the network, as the design gave a score of 70%, 80%, and 90% for load testing, stability, reliability testing and usability testing, respectively. Also, the bot is compatible with different operating systems, both for forward compatibility and backward compatibility having a score of 95%. The bot was able to answer customer questions, enquiries and complaints and the response time of the bot depends on the strength of the network since it is web based. Hence, the system provided a simple, cheaper, and durable customer financial and payment application service. It is therefore recommended that any company incorporating a chatbot should make sure that the chatbot is highly secure due to attacks and routine queries. It should also be standardised to deliver a high level of performance since chatbots will not be able to solve all queries. Keywords: Chatbot, Customer Support, Google Script, Online Service, Testing Word Count: 298 Words
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    based Predictive Model for Keylogging Attack Mitigation
    (Lead City University, 2023-12) Mariam Ayobami GBADEGESIN
    In the ever-evolving landscape of cybersecurity, the relentless progression of cyber threats presents an ongoing challenge to the integrity of sensitive data and user credentials. Among these threats, keylogging malware has emerged as a particularly insidious vector, adept at covertly infiltrating systems, stealing login credentials, and exfiltrating valuable information. This research is driven by the imperative need to confront this menacing adversary. By delving into the subtle intricacies of human keystroke dynamics, we have engineered a groundbreaking and intelligent predictive model aimed at the early and reliable detection of keylogging attacks. The innovative character of this model stems from its amalgamation of two powerful techniques: adaptive neural networks and fuzzy logic inference. This research develops a Neuro-fuzzy predictive model using keystroke dynamics to reliably detect and mitigate ongoing keylogging threats. The model’s training process was conducted using a diverse dataset comprising over three hundred thousand keystroke samples, sourced from both simulated users and actual keyloggers. Impressively, baseline neural networks exhibited a detection accuracy rate of 99.1%. Building upon this solid foundation, the specialized Neuro-fuzzy model further elevated precision, achieving a remarkable 99.62% accuracy. This enhancement primarily stemmed from the model’s ability to distinguish between human and automated keystroke patterns, significantly reducing false positives. These results demonstrate that an adaptive Neuro-fuzzy model can reliably predict keylogging attacks in real-time based on anomalous keystroke dynamics before significant credentials or data are exfiltrated. The adaptive model provides a robust predictive solution to a rapidly evolving risk that continues to bypass traditional reactive defenses. Keywords: Neuro-fuzzy, Neuro-fuzzy model, keylogging, keylogging threats Total word count: 250 words
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    Comparative Performance Evaluation of Random Forest on Web-based Attacks
    (Lead City University, 2023-12) Oluwaseye Abayomi ADEYEMI
    As human resources try to break into networks, control systems, and steal information with the help of expanding data communication paths and protocols, cyber intrusions are currently on the rise. The majority of typical online attack methods are thoroughly researched and documented. Countries, corporations, people, and vital infrastructures that depend on information technology for daily operations have suffered financial losses, the loss of personal information, and economic harm as a result of web-based intrusion. However, foreseeing an attack before it happens can aid in its prevention. This research proposes a predictive model for web-based attacks and a performance comparison of random forest with and without feature selection to secure the availability, integrity, and secrecy of networks, computer systems, and their data. The CIC-Bell-IDS2017 dataset, which includes typical and contemporary intrusion attacks, served as the raw data source for the proposed model. A python-based programming environment and interface for Anaconda Navigator, Jupyter Notebook, was used to create the predictive models. Performance evaluation and comparative analysis were conducted, and the results demonstrate that, once big data analytics (feature scaling and feature selection) were applied to the dataset, the models' prediction accuracies improved, creating a potential intrusion detection system. The outcome yielded excellent accuracy and model development times in both cases, with 97% and 98% precision for both sets and model development times of 35 seconds for the raw set and 15 seconds for the reduced set, which is an important factor when deploying machine learning models in a real-time setting. Random Forest is more computationally expensive than Correlation feature Selection-based classifiers, but having higher predictive accuracy, according to a comparison. Both of these methods work well and each has advantages and disadvantages. The use of big data analytics (PySpark) was found to help machine learning models perform better, resulting in better intrusion detection system. Keywords: Web Based Attacks, Random Forest, Correlation Feature Selection, Word Count: 300
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    Cybercrime Monitoring System for Online Security Expert
    (Lead City University, 2022-12) Cybercrime Monitoring System for Online Security Expert
    Cyberspace is increasingly attacked and there are limited means of mitigating this act. This is usually due to shifted degree of security features and management schemes within the cloud entity in cyberspace. The challenges are due to improvements in methods and the utilization of new technologies in committing crimes by criminals. The threat of cybercrime will continue to evolve and grow as criminals adapt to new security measures and take advantage of the changes in online behaviour. Hence, it is still challenging to identify and track down cybercriminals. This study focuses on implementing a cybercrime monitoring system for online data experts. After an in-depth understanding of cyber users' attacks, a possible solution could be proffered. A web application portal is designed using WordPress development tools that will serve as a platform to monitor and present possible vulnerabilities discovered. Tawk.to is integrated into the website for the implementation of real-life monitoring and My Structured Query Language is used as the database for storing and retrieval of information. This system will capture the digital signature of each piece of information sent to cyberspace, the user login parameter, the geographical location of the user, the Media Access Control address of the system used, the date, time and action carried out by the user while online. It will also aid cyber security experts in ascertaining the extent of activities carried out by cybercriminals in the domain. The result showed that the system can genuinely identify cyber users and their activities online. Keyword: Cybercrime, Cyberspace, Cyber Criminals, Security Expert Word Count: 247
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    Development of a Computerized Hospital Laboratory Operations’ Support System
    (Lead City University, 2022-12) Abiodun Timothy ADEGBIJI
    Manual hospital laboratory operation system is characterized with lack of prompt retrieval of Information which result in time wastage, loss of information, misplacement and misallocation of results. To proffer solution to the aforementioned problems, the research study developed a computerized hospital laboratory operation support application which is aimed at using information technology to solve the problems associated with manual method of Hospital Laboratory Information System. The system is a web based model built on laravel 7.29 and a WAMP (Windows, Apache, MySQL, PHP) server. A total of 4 laboratories were visited (2 government owned and 2 private laboratories) to collect various types of tests being carried out in the laboratories through ethical approval using the conventional and the developed systems. During the implementation of the developed system at Oyo state hospital management laboratory in Oyo and Ibadan, the system was installed unto the hospital laboratories’ database and subsequently utilized for registration of patients data and processed data. The result obtained showed that 65% of the respondents who were tested with the developed system used between 30-45 minutes and 48% used between 46- 60minutes while 88% that used the manual system used between 2-8hours before the result was ready.The adoption of this research will greatly allow prompt release of test results retrieval of Information, reduce patient test time wastage, give accurate laboratory test result, reduce loss of vital information, reduce misplacement of test results and reduce misallocation of test results to the barest minimum, if not totally eradicated. Keywords: Computerized, information technology, Hospital, Laboratory, Laravel Word Count:250
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    District Health Information System (DHIS2 SMS Server) and Routine Immunization (RI) Data in Goronyo LGA, Sokoto State
    (Lead City University, 2022-12) Bello A. SHEHU
    This project is on District Health Information System (DHIS2 SMS Server) and Routine Immunization (RI) Data in Goronyo LGA, Sokoto State. Descriptive research method was used to conduct the research. project is limited to Goronyo LGA, and its covers role of DHIS2 SMS server in strengthening Routine Immunization data in Goronyo LGA, Sokoto state. The target population for this research are mainly any health facility worker who is supporting/attached direct to Routine Immunization unit (Health facility in-charge, Routine Immunization service provider, Assistant RI service provider and RI recorder), working in public health facilities in Goronyo LGA (all 22 health facilities conducting RI in the LGA), sampling method was not use because the researcher was able to reach out all the respondents. Accordingly, 90 questionnaires were issued out to 90 RISP we have in the LGA. Of these figure only 85 were retrieved. Percentage was main method used to analyze the data. Similarly, data was downloaded from DHIS2 RI The research find out that 13 (15%) of respondents know DHIS2 before 2017, while 67 (85%) don’t know, but 85 (100%) of respondent received training on DHIS2 during implementation of the software. The significant of this study is to investigating District Health Information System (DHIS2 SMS Server) and Routine Immunization (RI) Data in Goronyo LGA, Sokoto State, which will serves as an addition of knowledge on District Health Information System (DHIS2 SMS server) in the LGA, state and nation in general, similarly, other impact this study, included, it shows how the software helps in RI data analysis, interpretation, storage, retrieval and transmission automatically, this research find out how the DHIS2 SMS server detect incompleteness and error of RI data, the research reveals how the software help health care mangers with data for monitoring and supervision from anywhere. Keywords: Routine Immunization, District Health Information System (DHIS2), Vaccination, ICT Infrastructure, Routine Immunization Data Word Count: 295
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    E-Health Data Security Using Hybrid Encryption Techniques
    (Lead City University, 2022-12) Ridwan Olayinka KOLAPO
    The introduction of cryptography has brought plenty of improvement to health informatics, but not really employed in health institutes and this is often as a results of the protection issues that come together with the employment of e-Health systems. Security issues is taken into account a significant concern which is why health institutes still opt to follow the normal way of addressing health records. This study aim to ensure data security in health record using enhanced Ceasar Cipher, Affine Cipher and Red2 Cryptographic Techniques.This method used is considered a double enhanced encryption algorithm with a steganography technique because it combines an enhanced Ceasar encryption method with Affine Cipher encryption technique to enhance security of medical health records along with Red2 algorithm, which helps encrypt cipher-text messages and hide them in images. The integration of the enhanced Caesar and affine ciphers is as follows: First, each field in the record is encrypted with the enhanced Caesar cipher encryption method, and the encrypted output is used as the input for the affine cipher encryption method after-which the output of the second encryption phase is fed into the steganography stage and the final output of the developed system is presented as an image.The result of this study is presented as a software solution and also some metrics like accuracy, precision, recall and F1-score were measure to test the performance of the system and it shows that the enhanced ceasar cipher encryption technique used in this study has a larger computational time which compensate for the high level of security and also the image used for steganography resulted to having lesser image quality parameters as this also compensate for the addition security level. In conclusion, the main aim of cryptographic system and steganographic system is to protect the confidentiality of data both at rest and in transit. With the mix of the enhanced Ceasar Cipher method and Affine cipher alongside Red2 Algorithm, these techniques has helped to enhance effectiveness. Keywords: e-Health, cloud, encyrption algorithm, Ceasar Cipher, Affine Cipher, Red2 algorithm. Word Count: 338
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    Electromagnetic Radiation and Spatial Proximity of Mobile Communication Base Stations: Analysis of Compliance in Sagamu Metropolis
    (Lead City University, 2022-12) Olabisi Olayinka, ONALAJA
    Electromagnetic radiation emanating from randomly selected 113 GSM Mobile Base Transceiver Stations (MBTSs) in different regions of Sagamu, Ogun State, Southwest, Nigeria, was assessed according to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines, National Communication Commission (NCC), National Environmental Standards and Regulations Enforcement Agency (NESREA). This was to determine the exposure level at these MBTS and their compliance to setback distance in relation to the specification in the guidelines. Measurements of the maximum Power Density of radio signals were taken for sites operating in GSM 900MHz, GSM 1800MHz, WCDMA 2100MHz and correlated with the ICNIRP, NCC and NESREA specifications. The result indicated that only 23.9% (27) of the entire MBTSs complied with NCC regulations (5m) set back to the closest infrastructure, while majority 76.1% (86) of the MBTSs do not comply. 62.8% (71) of the MBTSs complied with the NESREA standard of 10 metres set back to the closest infrastructure while 37.2% (42) do not comply with the regulations as they do not observe 10 meters set back from the nearest infrastructure. Only 6.2% (7) of the total MBTSs in the study area, violated the recommended E(V/m) for 900MHz, GSM 1800MHz, WCDMA 2100MHz rates having a peak value of 85V/m. Also, 25.7% (29) of the total MBTS in the study area violated the recommended power density levels for 900MHz, GSM 1800MHz, WCDMA2100MHz rates having highest value for power density which is (47.75mW/m2) while others also showed high values ranging from 9.966 to 29.73mW/m2. These findings suggest that many MBST’s complies with NESREA (10m) setback regulation but violated the NCC (5m) regulation. Radiations emanating from the accessed base stations in some vicinity are in safe range specified in the guidelines and as such they do not constitute health risk in the short run. Mobile base stations whose RF radiation intensity is significantly high once identified, the settlers should be advised to relocate away from such base stations. Keywords: Electromagnetic waves, Mobile Phone, Base Stations, power density, radiation level. Word Count: 299
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    Employee Attendance Tracking Using Facial Recognition System
    (Lead City University, 2023-12) Bukola Meka OWOLABI
    Traditional pen-and-notebook methods for employee attendance are often susceptible to inaccuracies and falsifications. Biometric systems, despite being more secure, confront issues such as high acquisition costs and inefficiencies in capturing fingerprints, especially when hands are unclean or injured. In this study, a cutting-edge Employee Attendance Tracking System using Facial Recognition is developed, addressing the shortcomings of conventional attendance methods and biometric systems. The proposed system employs an array of Python libraries including Django, face_recognition, OpenCV (cv2), numpy, and PCA. These libraries are utilized for their strengths in image processing, facial recognition, and efficient data management. The primary objective is to create a reliable, cost-effective, and efficient alternative for recording employee attendance, overcoming the limitations of existing methods.The system utilizes advanced image processing techniques to tackle common challenges in facial recognition, such as noise interference, varying lighting conditions, and physical obstructions like occlusions. This is achieved through innovative approaches like noise reduction, illumination normalization, and occlusion handling, significantly improving the accuracy of facial recognition under diverse environmental conditions. A key component of the system is the "Capture_Image" module, which establishes a reference database by capturing and storing employee images. Concurrently, the "Recognize" module employs machine learning algorithms for facial recognition, ensuring accurate and timely recording of attendance. The effectiveness of the system is demonstrated in its ability to adapt to a variety of environments, attributed to its advanced image processing capabilities and robust algorithmic framework. This innovative system is particularly advantageous for institutions, corporate offices, and industries seeking secure, precise, and efficient attendance tracking solutions. It marks a significant advancement in the field of attendance management, offering a blend of enhanced security, accuracy, and operational efficiency. The study recommends further enhancements, such as incorporating advanced algorithms to improve recognition accuracy in different lighting and noise conditions. Keywords: Accuracy, Biometric system, Employee Attendance Tracking, Facial recognition, Machine learning algorithm Word Count: 295 Words
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    Enhanced School Management System using Progressive Web Application
    (Lead City University, 2022-12) Emmanuel Adebowale ADEDIRAN
    School Management System using Progressive Web Application is aimed at Network Independent for Student Management system which allows the students to check through their details awithout necessary using data logging-in via their matriculation number, since the developed System will not allow matriculation numbers outside Lead City University. The main goal of this thesis is to develop a Progressive Web Application School Management System and provide an interface that will be used for all the necessary details of School Management. The objectives in achieving the set goal are: Designing of the database, designing of the design of the progressive web application school management system and the development of the whole system with HTML, CSS, PHP, JavaScript, React, Xamp Server. The system is targeted to helping students take their studies in a more advanced fashion breaking away from the conventional way of using papers and manual processing of doing their registration all the time. The School Management System using Progressive Web Application makes use of features such as service workers, push notification, indexedDB, Background sync is deployed for the implementation of the system. The system will give a more developed and user-friendly access as well as it will take less time to complete its execution. The cost of implementing the system is minimal compared to the cost of other development of other School Management System. The system is deployed into the Department of Computer Science in Lead City University. Keywords: Progressive Web Application, , School Management System. Word Count: 238