According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for ~ 10 million fatalities at the end of 2020. The most common types of cancers include Lung, Breast, CNS, Leukemia, Colon, and Cervical Cancer. Early detection of cancer can decrease the death toll. According to the study, if the cancer is identified at its early stage, the death rate can be reduced to ~85%. In order to reduce the death toll, machine learning (ML) emerges as a significant solution. When it comes to cancer research with ML, biopsy and microarray data come into the front. The biopsy data is less useful as it excludes a patient’s genetic information. However, due to the genetic information, the microarray data emerges as a solution to detecting cancer disease. Dealing with microarray data also has some consequences, and high dimensionality is one of them. This article reports an ML-based ensemble model to tackle microarray data issues and provide an effective model for cancer detection. The reported model uses Minimum Redundancy Maximum Relevance (MRMR) as the feature selection algorithm. The Whale Optimization Algorithm (WOA) is implemented for the featured dataset to select the optimistic number of features without affecting microarray data relevance. Then, four ML-based classification models, including Support Vector Machine, Decision Tree, Multi-Layer Perceptron, and Random Forest, are applied as the base learners to make initial predictions. Finally, the voting ensemble technique is applied to the initial prediction to develop an ensemble model for cancer prediction. The proposed En-MinWhale model is evaluated over six different types of cancer microarray datasets, including Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer. Finally, the performance of the proposed model is evaluated using 11 various evaluative parameters, including accuracy, precision, specificity, sensitivity, F-β score, etc. The En-MinWhale model shows 94.09%, 95.83%, 94.86%, 95.00%, 94.85%, and 96.77% accuracy for Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer datasets, respectively, that outperforms other considered hybrid models and can help out the physicians in Cancer diagnosis.

For developing nations like Ethiopia, the idea of human-machine interaction (HMI) in the burgeoning metaverse offers both potential and problems. It is critical to comprehend the changing dynamics of HMI in this new digital arena This chapter examines the current situation of HMI in Ethiopia, including its possible advantages, difficulties, and moral implications. The backdrop gives a summary of the metaverse and its expanding influence, proving that HMI analysis is necessary in this revolutionary technological environment. The chapter looks at several uses of the metaverse, ranging from social interaction and entertainment to virtual labor and education. It examines the potential that mobile apps provide to Ethiopia. But the conversation also explores the difficulties brought on by practical problems. With an emphasis on the Ethiopian experience, this chapter provides an extensive analysis of the changing HMI dynamics inside the metaverse. It seeks to add to the current conversation about the ethical advancement and use of metaverse, especially with regard to impoverished countries.

Purpose: The current study is the outcome of a thorough evaluation of the literature on functionalism in human resource management as it relates to organizational performance. It examines the role of human resource management and looks into various issues with human resource management in firms in such situations. Research Methodology: The theoretical underpinnings are accomplished through a systematic review and subsequent thematic content analysis of literature, including reports from published literature and research papers that have been published. These were critically examined, and the best matching documents were then carefully chosen for inclusion to reach a conclusion. Result: The result emphasizes the necessity for human resource management managers to implement cutting-edge strategies for faculty and staff empowerment so they may traverse the fast-changing environment and perform to their full potential against the odds. To engage stakeholders in amicably avoiding recalcitrance, a conceptual qualitative framework is established and recommended for the functionalism of tailored human resource management. This approach can help managers of human resources find opportunities during this crisis and help them come to significant conclusions about how to address issues and create a positive culture. Limitation: Risks of bias such as selection bias, insufficient blinding, attrition bias, and selective outcome reporting may be among the limitations. Future Implications: By working together to overcome predicted difficulties, this framework can help an educational leader manage stakeholders' expectations and benefit academics and practitioners alike. Novelty: Research novelty was a brand-new concept that might result in new knowledge or contribute to already-existing knowledge. Keywords: 1. Human Resource Management 2. Functionalism 3. Organization Performance 4. Systematic Literature Review

The primary goal of the chapter was to examine how, in the Ethiopian environment, an employee's work attitude and AI-based reinforcement learning function as mediators between their psychological intrinsic reward system and the perception of organisational support they receive from their employer. Based on their contribution to the GDP of the economy, the textile industries operating in Ethiopia's industrial parks were chosen as the study area. A quantitative research technique and explanatory research design were employed. A multi-phase sampling method was suggested. To assess discriminant and convergent validity, exploratory confirmatory analysis was carried out using AMOS software. It was discovered that the relationship between an employee's psychological intrinsic reward system and the organization's perceived organisational support was extremely poor in the absence of AI-based re-enforcement learning and the employee's work attitude as a mediator. Thus, the work attitude of employees and artificial intelligence (AI) function as a complete mediator.