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The study examined the comprehensive assessment of the efficacy of Library and Information Science (LIS) CPD programmes in the particular setting of Indian academic libraries in Northern India. The study systematically assessed the programmes' impact on four levels: behaviour, reaction, learning, and results, using Donald Kirkpatrick’s widely recognised evaluation model.

The research employed a census sampling method and a questionnaire to gather information from 177 respondents employed in university libraries.

The findings demonstrated that professionals were satisfied with the CPD programmes, eager to learn more and apply their newly acquired knowledge and skills at their workplaces, and interested in applying learning to get results. Moreover, the significant factors that hindered the implementation of learning in the workplace were a lack of management support and poor IT infrastructure.

The paper’s uniqueness and significance come from carefully examining the effects of CPD programmes in LIS within the particular setting of university libraries in Northern India.

The article investigates the wealth generation from takeover bids through an event study, analyzing the impact of announcements on stock returns of target and bidder firms across the industries and related and unrelated industry acquisitions. It aims to provide insights into financial implications for shareholders and market participants, contributing to understanding merger dynamics and informing investment decisions.

The methodology involves data collection of announcement dates, defining event and estimation windows. Market models and four-factor models are applied to compute abnormal returns. Linear regression estimates return, which is aggregated and tested for significance, providing insights into the wealth effects of takeover announcements.

Analysis reveals positive returns for both firms' shareholders on the announcement day, particularly significant for target firms. Pre-announcement, positive abnormal returns suggest potential information leakage, but reverse post announcement. Comparative model analysis emphasizes the role of systematic risk. Notably, a prolonged bidding process benefits the target firm. Trading in target company stocks under unrelated industry acquisitions appears to be more beneficial during the bidding.

This article introduces a novel approach by utilizing a four-factor model for computing abnormal returns, unlike previous research relying solely on the market model. It also focuses separately on related and unrelated industry acquisitions. This methodology captures comprehensive systematic risk, resulting in more conservative and robust abnormal returns. This methodological advancement addresses existing gaps in the literature and provides actionable insights for stakeholders in mergers and acquisitions.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.