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From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection
Get PDFGene microarray classification is yet a difficult task because of the bigness of the data and limited number of samples available. Thus, the need for efficient selection of a subset of genes is necessary to cut down on computation costs and improve classification performance. Consistently, this study employs the Correlation-based Feature Selection (CFS) algorithm to identify a subset of informative genes, thereby decreasing data dimensions and isolating discriminative features. Thereafter, three classifiers, Decision Table, JRip and OneR were used to assess the classification performance. The strategy was implemented on eleven microarray samples such that the reduced samples were compared with the complete gene set results. The observed results lead to a conclusion that CFS efficiently eliminates irrelevant, redundant, and noisy features as well. This method showed great prediction opportunities and relevant gene differentiation for datasets. JRip performed best among the Decision Table and OneR by average accuracy in all mentioned datasets. However, this approach has many advantages and enhances the classification of several classes with large numbers of genes and high time complexity
Detection of citrus diseases using artificial intelligence: A systematic review
Get PDFEarly detection of citrus diseases is important for the global agricultural industry, facing threats such as Huanglongbing and canker. This study reviews the current status of the use of artificial intelligence to improve detection accuracy and speed. A systematic literature review was conducted from 2019 to 2023, using databases such as Scopus, IEEE Xplore and ACM, focusing on identifying the fruits studied, prevalent diseases, AI algorithms used and their accuracies, as well as technical challenges in implementing AI systems. The results highlight that oranges, lemons and mandarins are the most investigated fruits, with Huanglongbing, black spot and canker as the most studied diseases. AI algorithms such as Deep Neural Networks (DNN) and Adaboost show high accuracies, essential to improve disease detection. However, challenges include lack of labeled data, adaptation to different agricultural conditions, and effective integration in dynamic agricultural environments. This study reveals the need to advance data quality and algorithm adaptability to strengthen sustainability and efficiency in disease detection in citrus crop
English Title: The State of Quantum Computing: Hardware, Algorithms, and Emerging Networks
Get PDFThis review article examines the current landscape and recent advancements in quantum computing, emphasizing its roots in quantum mechanics and its growing influence across various computational fields. A thorough analysis of recent literature, including academic publications and industry white papers, highlights significant progress in qubit technologies, quantum algorithms, and the emerging area of quantum networking. The findings indicate enhanced fabrication of quantum processors with higher qubit counts and improved stability and coherence. Additionally, developments in quantum algorithms suggest the potential for considerable speedups compared to classical methods for specific problems. Research into quantum key distribution and the prospect of a quantum internet points to promising advancements in secure communication. However, challenges surrounding error correction, scalability, and the practical implementation of quantum systems remain critical.
In conclusion, quantum computing is pivotal, showcasing tangible progress toward solving real-world problems. However, it continues to grapple with substantial hurdles in achieving fully fault-tolerant and scalable systems. Ongoing interdisciplinary research and development efforts are vital to unlocking this technology\u27s transformative potential and addressing its broader societal implications
African Education Systems in the Role of Artificial Intelligence (AI) in Automated Decision-Making
Get PDFThe main areas of focus for reforming educational systems are the incorporation of artificial intelligence (AI). Although artificial intelligence (AI) has many applications, its use in education to improve learning, develop employable skills, and help people adjust to life in the AI era has not been as common. The study evaluated current policy initiatives and delved into African information and communication technology (ICT) policies about the education sector to offer policy suggestions for AI educational institutions from an African viewpoint. The targeted population was junior and senior secondary schools in Kenya, a range of stakeholders in the field of education, including educators, parents, Ministry of Education representatives, and other pertinent parties. A sample of 125 participants was used. The study employed a descriptive research design. A combination of articles, research papers, reports, briefs, and books a along with interviews and questionnaires, was used to collect opinions and insights from these participants, and qualitative content analysis was involved. Its goal was to promote a thorough comprehension of the ethical issues related to the application of AI in the Kenyan educational system. The result indicated that 29 (28.2 %) were very familiar with automated decision-making, 63 (61.2 %) had reported some level of familiarity, while 11 (10.6 %) admitted did not know automated decision-making. The mean was fairly close to the true mean of the general population, as indicated by the extremely small total SE of 0.07. Drawing on the result, the report offers the policy priorities and suggestions that are crucial for establishing a supportive environment for the growth of AI and for governance in the African education sector
Navigating Education in the Age of Generative AI
Get PDFThe educational landscape is quickly evolving, presenting many opportunities. At the same time, there are tests to be passed when Generative Artificial Intelligence (AI) comes into the picture. Better rides going ways to alter education to enroll in the AI era, worth of the best integration of Generative AI technologies. To start, our deliberation will open discussions on how generative AI can precipitate authentic revolutions in the enhancement of learning experiences, customized tutorials, and generating very different contextualization. We continue to explore evils to the integration of AI that has cropped up as a result; issues of ethics, privacy, and educator training, all stand as major adversaries in this context. Therefore, our theoretical proposal is drawn from literature and empirical studies. It offers a structure by which lecturers or schools may integrate Generative AI effectively. The framework pertains to curriculum realignment, teacher training programs, augmented infrastructure, and a robustly piloted/code of ethics. We are further provoked to encourage and improve collaboration among scholars, technologists, policymakers, and stakeholders about ensuring the conscientious and ethical use of AI in educational settings. It is an asset valuable for educators, users, and policy developers keen on inserting the energy of Generative AI into the consistently disorderly order of the AI era. Cutting-edge methodologies and inclusive of a culture of adaptive change, education can now truly flourish in a world increasingly shaped by AI, supported by modern-day learners and teachers in the twenty-first century and beyond
From Past to Present: The Evolution of Data Breach Causes (2005–2025)
Get PDFThis review aims to analyze the changing causes of data breaches overtwo decades by synthesising evidence from various data breachinvestigation reports and regulatory filings. The methodology involvesexamining trends in threat actors, actions, and motives identified inreports such as the Verizon Data Breach Investigations Report (DBIR)series from 2008 to 2024, California Attorney General\u27s reports, and thePrivacy Rights Clearinghouse. (1,2,3) The findings reveal an evolutionthrough distinct phases: an initial period (roughly 2008-2010)dominated by external breaches leveraging hacking and malware, asubsequent era (2011-2019) marked by the rise of sophisticatedcybercrime, including increased phishing and the emergence of definedincident patterns, and a more recent epoch (2020-2024) characterisedby a significant surge in ransomware attacks, exploitation ofsystemic vulnerabilities, and the convergence of financially motivatedand nation-state actors. Throughout these periods, human factors anderrors have consistently contributed to successful breaches. In conclusion, the landscape of databreaches have shifted from simpler external attacks to more complex anddisruptive campaigns, where human vulnerabilities remain a keyenabler, and the emerging landscape includes AI-driven threatsthat are being explored by both attackers and defenders, necessitatingcontinuous adaptation of defence strategies to address both traditionalweaknesses and novel AI-related risks
Tinkercad and mBlock: Tools for Educational Robotics and Coding Learning A Case of Ghandi Primary school Morocco
Get PDFThis study explores the impact of Tinkercad and mBlock on the en gagement and computational thinking skills of students at Ghandi Primary School. Through interactive robotics and coding activities, students demonstrated increased motivation, problem-solving abilities, and confidence in programming. Using Arduino and block-based coding, they developed a deeper understanding of STEM concepts in a hands-on learning environment. The findings suggest that integrating educational robotics into primary education enhances student engage ment and fosters essential 21st-century skills
Artificial intelligence in Latin American higher education: implementations, ethical challenges, and pedagogical effectiveness
Get PDFArtificial intelligence is establishing itself as a catalyst for transformation in the regional university sector, generating growing yet uneven academic output. This research conducted a systematic review following the PRISMA methodology on applications of artificial intelligence in Latin American higher education. The results from the 421 studies obtained during the bibliometric stage indicate that research is geographically and institutionally concentrated in a limited set of approaches and practices. In this regard, a notable prevalence of studies on Machine Learning applications, as well as Natural Language Processing, was observed. From a practical standpoint, 30 studies were selected for qualitative analysis. These texts agreed that the implementation process of these technologies continues to face structural challenges. Notably, poor infrastructure conditions, as well as deficiencies in teacher training, were identified as the main obstacles to implementing these technologies. The analyzed studies also concurred on the inadequate treatment of algorithmic biases or data protection in application policies proposed by the literature. Consequently, a key recommendation of this research is the urgent need for studies aimed at evaluating short-term outcomes, as well as analyzing the long-term sustainability of such innovations
Integrating Traditional African Music into Modern Education Using Digital Platform and Artificial Intelligence
Get PDFThis study examined how traditional African music might be incorporated into contemporary education, emphasizing how it affects learning, preservation of culture, and educational development. It also examined recent studies on potential, difficulties, and implementation frameworks. Modern teaching techniques must be balanced with cultural authenticity for successful assimilation. Results indicate that students\u27 musical proficiency, cultural awareness, and cognitive abilities are all improved by traditional African music. Qualified teachers, useful instructional resources, utilization of artificial intelligence, and cultural preservation techniques are essential components of implementation. Cultural barriers, a lack of resources, and gaps in teacher preparation are still problems, but new technologies and creative teaching strategies provide answers. These problems must be addressed, especially the disparity in resources and quality of instruction between urban and rural areas. The study emphasizes how traditional African music shape’s cultural identity and aids in schooling. Through an analysis of how traditional African music promotes cross-cultural competency and cultural awareness in contemporary schooling, this study makes a distinctive contribution. Curriculum design, teacher education, and policy development gaps are filled by integrating educational frameworks, technological advancements, and cultural preservation techniques. The study offers suggestions for improving resource distribution, developing programs, and creating comprehensive curriculum guidelines that combine classic and contemporary teaching approaches in order to integrate music education globally. Curriculum design, teacher preparation, and educational policy are all significantly impacted by these revelations
AI in Dissertation Examination: Opportunities for Undergraduates and Postgraduates in Zambia, Rwanda, and Kenya
Get PDFThe integration of Artificial Intelligence (AI) in dissertation examination presents a transformative opportunity for higher education institutions in Zambia, Rwanda, and Kenya. As student enrollments continue to rise, universities face challenges in efficiently evaluating dissertations while maintaining academic integrity. AI-driven tools offer innovative solutions by automating tasks such as plagiarism detection, language quality assessment, and contract cheating identification. This study aims to explore the opportunities, challenges, and impact of AI adoption in dissertation assessment across selected universities. A mixed-methods research design was employed, incorporating surveys, semi-structured interviews, and data analysis from AI-assisted dissertation evaluations at Copperbelt University (Zambia), the University of Rwanda, and Jomo Kenyatta University of Agriculture and Technology (Kenya). Findings indicate that AI enhances efficiency by reducing faculty workload and improving feedback quality for students. However, challenges such as digital literacy gaps, infrastructure limitations, and concerns over AI’s fairness and ethical implications hinder full adoption. Despite these obstacles, there is strong support among students and faculty for AI integration, provided it is complemented by human oversight. The study concludes that AI has significant potential to revolutionize dissertation evaluation but requires investment in infrastructure, faculty training, and policy frameworks to ensure responsible implementation. Collaboration among universities, policymakers, and technology providers is essential to optimizing AI-driven dissertation assessment while upholding academic rigour