Kassim Kalinaki
@iuiu.ac.ug
Lecturer, Computer Science
Islamic University in Uganda
RESEARCH INTERESTS
Artificial Intelligence, Computer Vision, Ecological Informatics, Healthcare Informatics, Cybersecurity and E-learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Khairul Eahsun Fahim, Kassim Kalinaki, L.C. De Silva, and Hayati Yassin
Elsevier
Kassim Kalinaki, Owais Ahmed Malik, Daphne Teck Ching Lai, Rahayu Sukmaria Sukri, and Rodzay Bin Haji Abdul Wahab
Elsevier BV
Auf Tumwebaze Alicon and Kassim Kalinaki
Emerald
PurposeDespite the sporadic evolution of artificial intelligence, the most valuable asset of any organization in the modern world is human resources. This study aims to reveal that partnerships between higher education institutions (HEIs) and employers will ease the process of employee mid-career development in Uganda's corporate employment sector by promoting work-based postgraduate training, and this additionally promotes human resources (HR) capacity-building for organizations.Design/methodology/approachThe hypothesis is that contemporary employees seek out an academic mid-career development postgraduate programme that is blended to fit into the employees' work schedule. The study was a descriptive quantitative study, and a closed-ended questionnaire was sent out to groups of corporate employees online (N = 70) and 41 responded, giving a response rate of 58.5%.FindingsFindings indicate a need for a flexible program for mid-career development and transition, the low standard deviation of (Neutral = 0.95, Disagreed = 2.64 and Agreed = 3.3) implies an insignificant deviation from the mean of responses. Indeed, over 95% agree that pursue further studies is needed but in a more flexible way.Research limitations/implicationsThe study design was limited by the sample selection process and study design. In the future, the authors recommend a mixed study for both quantitative and qualitative dimensions of such studies.Practical implicationsIrrespective of gender, hierarchy and experience, employees want flexible study modes for their postgraduate. This implies that institutions of higher learning should work with the labour industry and position themselves as work-based information and communication technology (ICT)-Integrated learning theatres.Originality/valueThe move towards a collaborative strategy between academia and the employment industry is very evident in this study.
Wasswa Shafik and Kassim Kalinaki
IGI Global
This chapter explores the growing use of technology in various aspects of people's lives and focuses on smart cities. First, it provides a comprehensive survey that examines the need for smart cities, their architectural elements, and the characteristics and purposes of different architectural layers. The chapter also offers an overview of notable smart cities such as London, New York, Singapore, Busan, Amsterdam, and Sunshine Coast Regions, highlighting their unique features. Next, privacy and security concerns associated with smart cities are addressed, emphasizing the importance of privacy issues and suggesting potential solutions. The chapter discusses future research directions, including the integration of blockchains, security considerations, collaborative filtering, and infrastructure upgrades in smart city applications. The analysis of privacy and security concerns is organized into three subsections: smart city security traits, leveraging issues, and privacy challenges and solutions. Finally, the chapter concludes by presenting future research trends in this field.
Kassim Kalinaki, Owais Ahmed Malik, and Daphne Teck Ching Lai
Elsevier BV
Kassim Kalinaki, Sumaya Namuwaya, Aminah Mwamini, and Sarah Namuwaya
IGI Global
Cutting-edge technological advancements in artificial intelligence (AI) and its associated technologies such as machine learning (ML) and deep learning (DL) have garnered immense attention from academia and the industry due to their ability to automate tasks previously executed by human beings in various sectors of the economy. In the business sector, there's an increased interest to explore how these emerging technologies can be deployed to enhance engagements between businesses and clients by automating their interactions. Fueled by the prevalence of a plethora of digital marketing data, these data-driven techniques can help businesses to provide faster and more efficient customer support, while also freeing up human staff to focus on more complex business-related issues. Accordingly, this chapter seeks to provide a compact review of the different AI-powered techniques and their applications in scaling up customer support, highlight existing challenges in their usage in the marketing domain along with proposing future research directions.
Abdullah Alabdulatif, Navod Neranjan Thilakarathne, and Kassim Kalinaki
MDPI AG
In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.
Ahmad Fathan Hidayatullah, Kassim Kalinaki, Muhammad Muzamil Aslam, Rufai Yusuf Zakari, and Wasswa Shafik
IEEE
Social media platforms like Twitter have become substantial sources of user-generated content, enabling people to easily express their emotions and opinions. However, this freedom has increased the spread of harmful content, such as abusive language, sexually explicit content, and hate speech. This poses challenges for content moderation and user safety. In order to guarantee a safer, more receptive, and more pleasurable online environment for users of all ages, it is essential to develop a system capable of recognizing abusive and sexually explicit material on Twitter. Despite the growing importance of content moderation, a research gap exists in Indonesian tweets, with limited comprehensive studies on negative content identification. This research addresses this gap by evaluating the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) models in the Indonesian context, which were primarily developed for English and other languages. This research aims to identify abusive, adult, and neutral content in Indonesian tweets by examining and fine-tuning BERT-based models to maintain a healthy online environment for optimal tweet classification. Based on our experiments, the BERT-based models showed promising results in detecting negative tweets. Among the BERT-based models, IndoBERTweet achieved the best precision, recall, and macro F1 scores with 97.03, 96.88, and 96.94, respectively.
Muhammad Muzamil Aslam, Ali Tufail, Zahoor Ahmed, Kassim Kalinaki, Muhammad Nasir, and Rosyzie Anna Awg Haji Mohd Apong
IEEE
The idea of an agent is useful for describing circumstances in which it is difficult or perhaps impossible for a single entity to gain all the necessary knowledge about the state of a system. The multiagent system is known to be useful in designing distributed solutions. Control action, data, or even both are distributed. In this research, we investigate the performance of vehicular networks based on deep multi-user reinforcement learning, where numerous V2V links use the already occupied V2I frequency spectrum, in terms of spectrum sharing and consensus. Goal is a multiuser strategy for reaching the spectrum that enhances network distributed behavior without any contact or message communication. Due to the large number of vehicles and to overcome the problem, we developed consensus and spectrum sharing algorithms based on deep multi-user reinforcement learning.
Musa Chemisto, Tar JL Gutu, Kassim Kalinaki, Darlius Mwebesa Bosco, Percival Egau, Kirya Fred, Ivan Tim Oloya, and Kisitu Rashid
IEEE
The integration of artificial intelligence (AI) in maternal health is a promising avenue for improving pregnancy, early childhood, and postnatal care. This systematic review analyzed 31 articles retrieved from Web of Science, PubMed, and Scopus, which were classified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and Mendeley referencing tool. Our interpretive study found that AI applications in maternal health can predict 48% of maternal complications using electronic medical records (EMR), 29% using medical images, 19% using genetic markers, and 4% using other medical features such as fetal heart rates and sensors. The accuracy of prematurity prediction using AI was 95.7%, while the XGBoost technique predicted neonatal mortality with 99.7% accuracy. The study underscores the potential benefits of AI in maternal healthcare and highlights the need for further research to improve maternal and child health outcomes, especially in resource-constrained sub-Saharan African regions where maternal mortality rates are significantly high.
Kassim Kalinaki, Wasswa Shafik, Tar J. L. Gutu, and Owais Ahmed Malik
IGI Global
The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in this field would find the discussions in this chapter insightful.
Kassim Kalinaki, Navod Neranjan Thilakarathne, Hamisi Ramadhan Mubarak, Owais Ahmed Malik, and Musau Abdullatif
Springer International Publishing