AI-Driven Malware Detection and Classification: A Systematic Review of Techniques and Effectiveness (Published)
In addition to classifying malware, it was further observed that malware analysis experts have developed new methodologies and strategies to assess the composition of malware samples by comparing their behavior and features to several known malware families. Thus, this study examined AI-driven malware detection and classification, using the systematic review of literature to understand the techniques and effectiveness of the AI systems. The study adopted the meta-synthesis research design. Meanwhile, the PRISMA chart was used for the selection of literature. There were identified inclusion and exclusion criteria that were outlined to determine the literature that are relevant to the study. Results showed that the techniques used in AI-driven malware detection and classification systems include deep learning techniques, machine learning techniques, and hybrid models. The findings showed that the AI-driven malware detection and classification system is highly effective in detecting and classifying malware. Findings of the study showed that the evaluation strategies for AI-driven malware detection and classification include standard metrics, benchmark datasets, experimental comparisons, and cross-validation. Results showed that the challenges associated with the use AI-enhanced systems to detect and classify malware include computational complexity, interpretability, dataset limitations, adversarial attacks, and real-time deployment constraints. The study concludes that AI-driven malware detection and classification systems have different techniques and they are highly effective. It was recommended that there is a need for continuous update of datasets to reflect new attack vectors.
Keywords: AI-driven malware, Malware, artificial intelligence (AI), malware classification, malware detection
Human-Robot Interfaces: A Comprehensive Study (Published)
Human-Robot Interfaces (HRIs) are pivotal in bridging the gap between humans and robots, enabling intuitive communication and collaboration. This manuscript examines the principles, advancements, applications, and challenges of HRIs, delving into technologies like speech recognition, gesture interpretation, haptic feedback, and brain-computer interfaces. Applications in diverse fields such as healthcare, manufacturing, education, and space exploration are discussed, emphasizing the role of AI, multimodal communication, and ethical considera- tions. Supported by relevant research and examples, this study highlights how HRIs are shaping the future of human-robot interaction
Keywords: Gesture-Based Interaction, Human-Robot Interfaces (HRIs), Machine Learning in Robotics, Robotic Pro- cess Automation (RPA), artificial intelligence (AI)
Ethical AI in Retail: Consumer Privacy and Fairness (Published)
The adoption of artificial intelligence (AI) in retail has significantly transformed the industry, enabling more personalized services and efficient operations. However, the rapid implementation of AI technologies raises ethical concerns, particularly regarding consumer privacy and fairness. This study aims to analyze the ethical challenges of AI applications in retail, explore ways retailers can implement AI technologies ethically while remaining competitive, and provide recommendations on ethical AI practices. A descriptive survey design was used to collect data from 300 respondents across major e-commerce platforms. Data were analyzed using descriptive statistics, including percentages and mean scores. Findings shows a high level of concerns among consumers regarding the amount of personal data collected by AI-driven retail applications, with many expressing a lack of trust in how their data is managed. Also, fairness is another major issue, as a majority believe AI systems do not treat consumers equally, raising concerns about algorithmic bias. It was also found that AI can enhance business competitiveness and efficiency without compromising ethical principles, such as data privacy and fairness. Data privacy and transparency were highlighted as critical areas where retailers need to focus their efforts, indicating a strong demand for stricter data protection protocols and ongoing scrutiny of AI systems. The study concludes that retailers must prioritize transparency, fairness, and data protection when deploying AI systems. The study recommends ensuring transparency in AI processes, conducting regular audits to address biases, incorporating consumer feedback in AI development, and emphasizing consumer data privacy.
Keywords: Data protection, Fairness, algorithmic bias, artificial intelligence (AI), consumer privacy