https://repository.ju.edu.et//handle/123456789/1211 2026-04-17T10:04:38Z 2026-04-17T10:04:38Z Sekaran, S. Chandra C, Natarajan S, Esakkiammal et al. https://repository.ju.edu.et//handle/123456789/9527 2025-04-17T11:23:30Z 2024-01-01T00:00:00Z

Nonlinear Analysis and Topological Approaches towards a Deep Intelligent Framework for Privacy Assurance of Autonomous IoT Systems Sekaran, S. Chandra; C, Natarajan; S, Esakkiammal; et al. The proliferation of autonomous Internet of Things (IoT) systems powered by deep learning and artificial intelligence has ushered in a new era of data-driven convenience and automation. However, this innovation comes hand in hand with heightened concerns regarding data privacy. This paper presents a comprehensive framework for Privacy Assurance in Autonomous IoT Systems (PAIS), which amalgamates cutting-edge technologies and best practices to safeguard individual privacy in the era of pervasive connectivity and autonomous decision-making. The PAIS framework comprises multifaceted strategies to address privacy challenges in autonomous IoT ecosystems. It leverages advanced encryption techniques, robust access control mechanisms, and anonymization protocols to ensure data confidentiality. Moreover, differential privacy mechanisms are deployed to protect the identities of individuals within data streams. An innovative aspect of PAIS is the integration of AI-driven privacy monitoring, which constantly evaluates data for potential breaches and triggers immediate responses when anomalies are detected. Ensuring regulatory compliance is a paramount facet of the PAIS framework, as it aligns with evolving data protection regulations globally. Users are afforded control and transparency through intuitive interfaces, enabling them to manage their data usage preferences effectively. The ethical implications of AI in privacy preservation are also examined within the framework, emphasizing the importance of fairness and bias mitigation. PAIS promotes a privacy-by-design approach, where privacy considerations are integral to the inception and development of IoT systems. Regular risk assessments are performed to identify potential privacy vulnerabilities, ensuring that the framework adapts to emerging threats. Education and training programs are provided to stakeholders to foster awareness and adherence to privacy best practices.

2024-01-01T00:00:00Z H/MICHAEL, BETHLEHEM https://repository.ju.edu.et//handle/123456789/9470 2025-04-07T12:03:39Z 2024-01-01T00:00:00Z

INVESTIGATING THE EFFECT of INFORMATION AND COMMUNICATION TECHNOLOGY TOOLS on QUALITY HEALTHCARE DELIVERY in THECASE of JIMMA MEDICAL CENTER H/MICHAEL, BETHLEHEM This study investigates the impact of Information and Communication Technology (ICT) tools on the quality of healthcare delivery at Jimma University Medical Center (JMC), the only teaching and referral hospital in southwest Ethiopia. Established in 1930, JMC serves a catchment population of over 15 million and operates with an 800-bed capacity. The purpose of this study is to offer evidence-based suggestions for optimizing JMC's use of ICT, with wider ramifications for enhancing healthcare delivery in comparable settings. The adoption of ICT tools, such as electronic health records (EHRs), Laboratory Information System, and health information exchange systems, has the potential to transform healthcare services by improving access, enhancing patient outcomes, and optimizing operational efficiency. The study tackles important issues about how ICT tools are currently used, how healthcare professionals view them, and how they affect patient outcomes including satisfaction and clinical effectiveness. To evaluate the current ICT infrastructure and pinpoint implementation obstacles, a mixed-methods approach is used, combining quantitative and qualitative data from patients, healthcare professionals, and administrative staff. The results suggest that although ICT technologies have demonstrated potential in improving the quality of healthcare, obstacles including a lack of technical know-how, infrastructure problems, and change aversion prevent them from reaching their full potential. The study provides valuable insights into ICT's role in healthcare, guiding operational strategies and policy decisions to improve patient care and system efficiency. The study underscores the necessity for ongoing training and support for healthcare professionals to effectively utilize ICT tools in healthcare delivery, focusing on user-friendliness, accessibility, secure communication, privacy, and security.

2024-01-01T00:00:00Z W/MESKEL, ELIAS https://repository.ju.edu.et//handle/123456789/9459 2025-04-01T11:33:51Z 2024-01-01T00:00:00Z

IDENTIFICATION and MEDICINAL ASSESSMENT of INDIGENOUS MEDICINAL PLANT LEAVES USING IMAGE PROCESSING TECHNIQUES W/MESKEL, ELIAS Indigenous medicinal plants hold immense therapeutic potential, yet their systematic identification and medicinal assessment remain underexplored. This study addresses the research gap by investigating the integration of traditional knowledge with modern technology to identify and assess the medicinal properties of plant species in the Yem Special Zone, Ethiopia. A novel Medicinal Assessment Framework (MAF) is developed using image processing techniques and machine learning classification to bridge the gap between traditional practices and contemporary scientific methodologies. The study employs a Design Science Research Methodology (DSRM), involving iterative cycles of development, evaluation, and refinement. Key steps include ethnobotanical data collection, digitization of traditional knowledge, acquisition and preprocessing of plant leaf images, feature extraction (shape, color, and texture), machine learning-based classification, and medicinal assessment scoring. Advanced image processing techniques such as Gaussian blur, Otsu’s thresholding, and morphological operations are used, while machine learning classifiers assess the extracted features. The results demonstrate the efficacy of the framework, achieving satisfactory accuracy in classifying 25 indigenous plant species based on leaf characteristics. The medicinal assessment scoring system combines traditional knowledge with quantitative analysis, offering a comprehensive evaluation of each species' medicinal potential. This integrative approach not only enhances the identification process but also contributes to the preservation and sustainable use of indigenous medicinal plants. In conclusion, the proposed framework provides a valuable tool for researchers, conservationists, and healthcare practitioners, promoting interdisciplinary research and community engagement. This work underscores the importance of leveraging traditional knowledge alongside cutting-edge technologies to preserve cultural heritage and biodiversity while advancing medicinal research.

2024-01-01T00:00:00Z YILMA, BEHREDIN REDI https://repository.ju.edu.et//handle/123456789/9433 2025-03-26T12:20:34Z 2024-01-01T00:00:00Z

NEXT WORD PREDICTION for SILTIGNA LANGUAGE USING RNN YILMA, BEHREDIN REDI Natural Language Processing (NLP) is a branch of Artificial Intelligence focused on the analysis and understanding of natural language. One primary application of NLP is next-word prediction. Which involves predicting the next word in a sentence by presenting a list of the most likely candidates for that position. Siltigna language is categorized into to Semitic language group which is spoken in the central Ethiopian regional Government by the Silte peoples. The language is characterized by unique syntactic and semantic structures and requires specialized models for effective language processing. Lack of next-word prediction model leads Siltigna language users to problems like more time consuming during writing, error-prone, spelling error and also physically disabled persons who have typing difficulties can ‘t use this language easily to communicate with each other. This study addresses this problem by proposing an approach to next-word prediction for the Siltigna language, by applying the power of RNN. The objective of this study is to investigate the possibility of building a next-word prediction model for the Siltigna language, using the RNN algorithm. To achieve the objectives, 70,434 sentence of data was collected from different sources. The corpus divided 80% into a training set for training the models and 20% into a testing set for testing the designed model. To get the optimal performing model, we executed 6 distinct experiments employing various advanced iterations of RNN in both singular instances and their hybrids, namely LSTM, BLSTM, GRU, BGRU, BLSTM-GRU, and BLSTM-BGRU utilizing collected and preprocessed datasets of Siltigna with different layers and hyperparameters. We evaluated the constructed model utilizing accuracy and categorical cross-entropy loss function. The proposed models were trained and evaluated with Siltigna sentences, and we acquired performance metrics of 94.4% BGRU, 92.14% BLSTM, 88.5% LSTM, 86.35% GRU, 83.54% BLSTM-BGRU and 81.89% BLSTM-GRU. The experimental findings substantiate that the architected BGRU network model surpasses all other conducted experiments and is identified and recommended for Siltigna next word prediction tasks, which yield encouraging results.

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