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While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational environment. The educational institutions need to distinguish students who are weak in their studies and require special attention and monitoring to improve their learning behaviours, future academic performances and factors that can affect their interpretation. This paper adopted a hybrid classification model using Decision tree and support vector machine (SVM) algorithms to predict students' academic performance. We statistically analyzed and identified factors that can affect students' academic performance. The dataset used is collected from Bachelor students of the City University of Science and Information Technology (CUSIT). The experimental results revealed 71.79%, 74.04% and 78.85% for decision tree, and 69.87%, 74.04% and 71.15% accuracy for SVM models respectively for different splits. The study identified seven different factors that can directly affect the students' performance associated with educational institutions and social networks. Factors like "time spent on social networks," "type of games playing on mobiles," and "time spent on playing mobile games" significantly affect students' performance.

Universities play a remarkable role in the development of a country by producing skilled graduates for the country. Graduation rate is low as compared to the enrollment rate in the higher education institutions. Academic failure is main reason for non-degree completion. Students' retention and high academic performance are significant for students, academic and administrative staff of universities. In this paper, our objective is to Predict the chance of students being at risk (AR) or not 'Not at risk' (NAR) with respect to their degree. Population of study consisted of all students of social sciences studying in 4th semester and they enrolled in 2007 session of BS and MA/MSc program at University of Gujrat Hafiz Hayat Campus. By using stratified sampling with proportional allocation method, a sample of 300 students was selected. We have used Multilayer Perception Neural Network Model to predict the chance of students being at risk (AR) or not 'Not at risk' (NAR) with respect to their degree on the basis of CGPA at the end of 2nd semester, Study time. Previous degree marks, Home environment, Study habits Learning skills, Hardworking and Academic interaction. In classifying the students at risk/not at risk, we could achieve a rate of correct classification of over 95% in training sample and over 85% in holdout sample. The estimated models can be used to predict the students being at risk or not with respect to their degree.

Purpose: This paper attempts to explore possible contributing factors of females' outperformance and males' underperformance in the higher education in Pakistan from teachers' perspective. The central question of the study is what are the key factors that affect female and male students' educational performance at the university level? Using Artificial Neural Network (ANN) as a framework, we attempted to predict differentials of the perceived "female outperformance" and "male underperformance" in higher education. We carried out the study by employing quantitative research methods. Design/methodology/approach: The data for the study come from 253 teachers from University of the Punjab--largest and oldest University in Pakistan. We used a structured questionnaire for data collection. The analysis was carried out with the help of ANN model. Statistical Package for Social Sciences (SPSS) was used to analyze the data. Findings: The testing results of ANN indicated 85.3% of teachers' perception was correctly predicted on various dimensions of performance differentials across female and male students in higher education. Research limitations/implications: The study banks on primary data collected from teachers of the University of University of the Punjab, Pakistan. Thus, the study's universe was limited to one university--University of Punjab. It is purely based on a quantitative approach employing ANN. Practical implications: The findings of this study have several significant implications, i.e. it makes a significant contribution to the existing body of scholarly texts on the issue of gender reverse change in academic performance in higher education. Originality/value: The findings of this research, derived from primary data in Pakistan context, qualify this research as an original one. We also claim that this study is one of the first studies on gender reverse change in academic performance among graduate students in a public sector university of Pakistan employing ANN.

This paper analyses Pakistan Federal Foundational Learning Policy (FFLP) 2024 in light of holistic education. While enhancing the literacy abilities of children is a notable objective of this policy, this study examines policy in terms of its potential for the student’s education as a whole. In this paper, critical discourse analysis was employed by Mullet, focusing on language, the text structure, and the suggested measures of implementation of policy. This study shows that though FFLP 2024 ensures that gaps in foundational learning are filled, it might downplay other elements necessary for early childhood, namely socio-emotional development, creativity, and cultural competence. This policy gap may impose limitations on school’s curriculum and teaching strategies as it centers on quantifiable results & standardized tests. The findings drawn from the analysis of the policy have questioned its suitability for different regions and its effect on the teachers’ professionals’ autonomy. The study proposed that a balanced approach that incorporates basic skills and general goals is needed for inclusive student development. The study provides valuable suggestions for a broader strategy towards the foundational learning. [ABSTRACT FROM AUTHOR]

Circular economy (CE) regenerates nature by introducing circularity in economic systems. It tackles various global challenges of sustainability, specifically in the agriculture and food sectors. Digital technologies support its implementations; especially, artificial intelligence (AI) is acquiring momentum. In this regard through algorithms, remote sensors, drones etc must be incorporated to achieve the desired target. The current studies provide bounded investigations of AI-driven CE exclusively in Pakistan. The main purpose of this paper is to elaborate on AI support in the implementation of CE practices and to explore the current waste situation in Pakistan and the implementations of CE and AI-driven CE practices in it. Inductive research is conducted in two stages. On the one hand, the theory is developed to evaluate the CE concept and AI techniques to strengthen its practices. On the other hand, a framework is proposed for multi-purpose case studies in the agriculture and food industries of Pakistan to integrate capabilities of CE and CE driven by AI. The outcomes of this research reveal that the true value of AI lies in the transition of CE and recommends that Pakistan must take some crucial measures to boost these practices to achieve sustainable development goals. Some limitations and future research proposals are also provided. The study helps researchers, companies and institues to participate positively towards the Circular Economy goal achievement by imlementing the AI. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive comparative analysis of the rights of prisoners in Pakistan, with a special focus on the alignment of these rights with Islamic law and international human rights standards. The study is motivated by the increasing concern over reports of human rights abuses in Pakistani prisons and aims to provide a thematic understanding of prisoners' rights within this specific context. The methodology adopted for this research is descriptive and involves a critical analysis of international human rights instruments, Pakistani laws, and Islamic teachings. The study also incorporates case studies and real-life instances to provide a grounded perspective on the practical application of these laws. The paper begins with an overview of the current state of prisoners' rights in Pakistan, outlining the existing legal framework and the realities within the prison system. It then delves into an analysis of prisoners' rights as delineated in Islamic law, emphasizing the humane treatment of prisoners and their access to legal representation, healthcare, and education. This is followed by a discussion of international human rights standards, particularly the Mandela Rules, and their application to the treatment of prisoners. A significant part of the research involves a comparative analysis of the rights of prisoners in Pakistan against the backdrop of Islamic law and international standards. This comparison highlights both congruities and disparities, underscoring the challenges in implementing these rights within the Pakistani context. The paper also presents case studies that illustrate violations of prisoners' rights in Pakistan, emphasizing the urgent need for reform. The conclusion and recommendations section synthesizes the findings, advocating for the implementation of Islamic law and international human rights standards in the Pakistani penal system. It calls for improvements in existing laws and policies to better safeguard the rights of prisoners. The study highlights the need for comprehensive reforms, increased awareness, and advocacy to bridge the gap between policy and practice in the protection of prisoners' rights in Pakistan. [ABSTRACT FROM AUTHOR]

The fundamental objectives of this research paper are to explore the enduring patterns of China's strategic competition in the Asia Pacific and to understand the interplay between states and their struggle for power from the lens of offensive realism. China and the US are currently the largest and the most powerful states. There was a time when the world was bipolar as the Soviet Union and the US during the Cold War were at loggerheads, but with the dismemberment of the Soviet Union, the US became the only superpower. However, in recent times, China has also demonstrated a promising rise in its economic and military power, and owing to its active engagement in Asia Pacific. In response, the US has also sharpened its interests in the given region. This research focuses on the broader analysis of China's Strategic Competition in Asia Pacific from the prism of Offensive Realism. This research has employed the qualitative method. Analytical literature (books and journal articles) for descriptive and historical research methods provide ample fresh information on the debate discussed in the research paper. Looking at the power play between the US and China, all states more or less have offensive capacities, but they fall into different levels of interest. With the realpolitik technique, China aspires to maximize cooperation with Russia, China, and India, though currently, China has flared territorial disputes with India. Through its economic and security capabilities, China would try to dominate the Asia Pacific region and exclude the US. [ABSTRACT FROM AUTHOR]

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%. [ABSTRACT FROM AUTHOR]

Purpose: The widespread integration of artificial intelligence (AI)-enabled personalization has sparked a need for a deeper understanding of its transformative potential. To address this, this study aims to investigate the mental models held by consumers from diverse cultures regarding the impact and role of AI-enabled personalization in their lives (i.e. individual well-being) and in society (i.e. societal well-being). Design/methodology/approach: This paper uses the theories-in-use approach, collecting qualitative data via the critical incident technique. This data encompasses 487 narratives from 176 consumers in two culturally distinct countries, Belgium and Pakistan. Additionally, it includes insights from a focus group of six experts in the field. Findings: This research reveals that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society. The particular aspects of the extended/restricted self and the affluent/ailing society that emerge differ across respondents from different cultural contexts. Originality/value: This cross-cultural research contributes to the personalization and well-being literature by providing detailed insight into the transformative potential of AI-enabled personalization while also having important managerial and policy implications. [ABSTRACT FROM AUTHOR]

This paper explores the criminal aspects of AI-generated fake news in Pakistan and Saudi Arabia, focusing on the challenges posed by the rapid spread of misinformation through social media platforms. The paper assesses the effectiveness of current legal and regulatory frameworks in these countries in deterring and punishing those who produce and disseminate fake news using AIbased technologies. By examining the existing legal provisions in both Pakistan and Saudi Arabia, the paper highlights the need for improvement in these frameworks to address the growing use of AI in generating fake news. The paper concludes that more specific and detailed legal definitions, stronger enforcement mechanisms, and greater collaboration between government agencies and civil society groups are required to combat AI-generated fake news effectively while protecting freedom of speech. Furthermore, raising public awareness through media literacy and critical thinking campaigns is crucial to building a more informed and resilient society. [ABSTRACT FROM AUTHOR]

The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

University students have used ChatGPT extensively to complete their academic tasks since its release in November 2022. The objective of the paper was to explore university students' perceptions regarding the role of ChatGPT in enhancing their academic performance and their concerns regarding the ethical and social implications of using ChatGPT in their academic tasks. The research design of the current qualitative study was phenomenology. Data were collected using semi-structured interviews with eight participants selected purposefully from a private-sector university in Lahore. We used NVivo software (version 14) for data analysis. Clarke and Braun's (2013) guide for thematic analysis was followed to read, review, and identify the codes, patterns, and themes. The analysis revealed that the majority of respondents believed that ChatGPT is a very effective tool that offers practical support to them in generating innovative ideas for solving academic problems. Students also showed serious concerns about the lack of clarity and policy guidelines for effectively using ChatGPT in writing their academic tasks. Based on the study findings, we suggest universities should develop and enforce a policy clarifying the limitations and scope of ChatGPT. [ABSTRACT FROM AUTHOR]

The coronavirus has created significant disruptions and exposed supply chain (SC) vulnerabilities. This crisis started a discussion about SC sustainability and performance. Therefore, the implementation of advanced technologies and supply chain collaboration could mitigate this disruption with the help of government support and policies. Considering this situation, this paper examines how COVID-19 influences advanced technologies (Artificial Intelligence, the Internet of Things, Blockchain, Digital twins, and Big Data Analytics) and supply chain collaboration (SCC) with a moderating role of government support and policies (GSP) in Pakistan. The study encompasses a comprehensive assessment carried out via structural equation modeling and data collected from Pakistani companies engaged in SCM or those operating within the SC divisions of manufacturing enterprises. According to the empirical findings, it is evident that COVID-19 outbreaks have a significant influence on SSCP; However, they do not show a similar impact on advanced technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration, the influence of COVID-19 on SSCP was effectively mediated through advance technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration. This research contributes to the existing literature on SSCP by emphasizing the importance of the resource-based view, dynamic capability view, and institutional theories. SC and logistics managers can apply the theoretical framework proposed in this study to mitigate the impact of the COVID-19 epidemic or disruptions in logistics and SC operations, thereby improving profitability in the context of an epidemic. [ABSTRACT FROM AUTHOR]

Public sector organizations are adopting Artificial Intelligence to gain efficiency, improve service quality and enhance policy-making capabilities. However, the growth of technology adoption in Pakistan's public sector organisations is slow because of several challenges the country faces. This paper focuses on the challenges that Pakistan's public sector organisations face in implementing AI and aims to highlight the initiatives in light of "Vision 2025," and "Digital Pakistan Policy 2018." In a nutshell, technological challenges like cost, budgets, technology adoption, research and development, cost-benefit analysis, collaborations, bureaucratic structures, and ICT readiness are the issues that are faced by public sector organisations. Policy-making in digitalisation and overcoming the adoption challenge is a big challenge for public sector organizations. This paper has identified some of the challenges in public sector organisations where further research and policy-making can help overcome challenges for public offices in the future. [ABSTRACT FROM AUTHOR]

Health and scientific researchers in non-English speaking countries such as Pakistan, are not proficient in English, which limits their ability to communicate their ideas and findings to the international scientific community. ChatGPT is a large language model that can help non-native English speakers to write high-quality scientific papers much faster by assisting them in conveying their ideas in a clear and understandable manner, as well as avoiding common language errors. In fact, ChatGPT has already been used in publication of research papers, literature reviews, and editorials. However, it is imperative to recognise that ChatGPT is still in its early stages, thus, it is important to recognise its limitations. It is suggested that ChatGPT should be employed to complement writing and reviewing tasks but should not be relied on to generate original content or perform essential analysis, as it cannot replace human expertise, contextual knowledge, experience, and intelligence. Researchers should exercise caution and thoroughly scrutinise the generated text for accuracy and plagiarism before incorporating it into their work. Key Words: Artificial intelligence, ChatGPT, Health research, Scientific research.

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the impact of big data analytics capabilities (BDAC) on organizational innovation performance through process-oriented dynamic capabilities (PODC), as a mediator, as well as the moderating roles of organizational culture (OC) and management accountants, in this artificial intelligence (AI) era. This paper also aims to provide information on the emerging trends and implications of the abovementioned relationships by focusing on these relationships and interactions. Design/methodology/approach: This exploratory study used the close-ended questionnaire approach based on the resource-based view and socio-materiality theories. This included sending questionnaires to top-level management, including Chief Financial Officer/Chief Executive Officers/Chief Information Officers (CFO/CEOs/CIOs), having an in-depth understanding of the concepts, practical applications and usage of big data as well as BDAC.181 valid questionnaire-based responses were analyzed using the partial least square structural equation modelling technique and bootstrapping moderated mediation method. Findings: This study provides empirical insights into how BDAC impact innovative performance through PODC as well as the moderating effects of OC and management accountants. This involves a shift in focus from almost standardized approaches to developing BDAC without contextual focus on approaches that are much more heterogeneously related to each organization and hence are more focused on the context of the pharmaceutical industry. Research limitations/implications: The main aim of key research questions in this study is to increase the contributions of BDAC toward improving innovation performance in the presence of the abovementioned variables and relationships that exist between them. The chosen research approach can be improved by carrying out interviews with the top management to obtain more relevant and detailed information for developing a better understanding of the abovementioned relationships. Practical implications: This study outlines how organizations that are developing BDAC approaches can focus on relevant factors and variables to help their initiatives and its role in organizational innovative performance. This will also help them develop sustainable competitive advantage in manufacturing concerns, specifically in the health industry, namely, the pharmaceutical industry. Originality/value: This study investigated the effects and implications of big data on organizations in the AI era that aim to achieve innovation performance. At the same time, it provides an original understanding of the contextual importance of investing in BDAC development. It also considers the role of management accountants as a bridge between data scientists and business managers in a big data environment, especially in the pharmaceutical industry. The current study used first-time data from surveys involving CFOs, CEOs or CIOs of pharmaceutical companies in Pakistan and analyzed the proposed model using bootstrapping moderated mediation analysis. [ABSTRACT FROM AUTHOR]

Pakistan is facing the deepest energy and economic crisis of its history. In fact, the ongoing economic crisis is more or less due to the energy crisis. In spite of this critical situation, Pakistan began from a meager 70 MW installed capacity at the time of the creation of the country and now has raised that capacity to 40,923 MW with a huge transmission network infrastructure based on 58,679 km transmission lines and a consumer base of 36.5 million. Despite this massive progress, there is a continued power deficit, mounting circular debt, and large losses, which all indicate a depleted picture of the power sector. This paper primarily undertakes the diagnosis of these crises and provides a basic assessment of demand-side management as a potential avenue to overcome energy crises. In this context, a detailed overview of the energy and power sectors of Pakistan, including the outdated T&D system, is undertaken. These diagnoses suggest that poor administration, governance, and inappropriate policies have contributed to these crises. In the meantime, efforts to overcome these crises with expensive capacity additions have also failed to address the energy crisis. However, a careful review of the literature and on-the-ground matters indicates that DSM is the most reliable solution. Sectoral DSM potential is estimated. Implementing the proposed measures will help greatly to overcome these crises. [ABSTRACT FROM AUTHOR]

Indian military strategy has tended to neglect the link between the conventional and nuclear domains in a nuclear weapons environment. We argue that this anomaly is evident in two broad areas: the conception of "war," and the complexity produced by new technologies that span the two domains. First, we show with empirical evidence that "limited war" in a nuclear environment is misnomer: the reality is more appropriately called "marginal conflict" owing to its extremely restricted nature. It follows that strategic planning and posture must be tailored accordingly. We then highlight the risk of escalation produced by conventional technologies that carry potential cross-domain nuclear effects, noteably with respect to cyber, artificial intelligence, missile defence and space. We note that the complex strategic effects produced also complicate military-strategic interactions traversing geographic domains, noteably South, Southeast Asia, and Northeast Asia. The paper concludes with some reflections on the reasons for these lacunae in Indian strategic thinking and what might be done about them. [ABSTRACT FROM AUTHOR]

The recent development of AI Chatbot – specifically ChatGPT - has gained dramatic attention from users as evident by ongoing discussion among the education fraternity. We argue that prior to making any conclusion, it is important to understand how ChatGPT is being used in higher education across the globe. This paper makes a significant contribution by systematically reviewing the global literature on the use of ChatGPT in higher education using PRISMA guidelines. We included 69 studies in the analysis based on inclusion and exclusion criteria. We presented the scope of published literature in three aspects: (i) contextual, (ii) methodological, and (iii) disciplinary. Most of the studies have been carried out in HICs (n = 53; 77%) representing the field of higher education (n = 37; 54%) without specifying the discipline, while only a few studies were based on empirical data (n = 19; 27%). The findings based on included studies reveal that ChatGPT serves as a convenient tool to assist teachers, students, and researchers in various tasks. While the specific uses vary, the underlying motivation remains consistent: seeking personal benefits and reducing academic burdens. Teachers use it for personal and professional learning and resource generation while students use it as personal tutors for various learning purposes. However, concerns related to accuracy, reliability, academic integrity, and potential negative effects on cognitive and social development were consistently highlighted in many studies. To address these concerns, we have proposed a comprehensive framework for universities along with directions for future research in higher education as an optimal response. [ABSTRACT FROM AUTHOR]

Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends. [ABSTRACT FROM AUTHOR]

Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model. [ABSTRACT FROM AUTHOR]

This paper's primary goal is to develop an agriculture drone for spraying pesticides. We discuss an architecture based on unmanned aerial vehicles (UAVs) in this study. Pesticides must be used in agriculture if the quality of largescale output is to be maintained. It is crucial to increase agriculture's production and efficiency by employing cuttingedge technology to replace employees with intelligent equipment like robots. The research suggests a novel method to replace people in a number of agricultural tasks, including the identification of insect presence, the application of pesticides and fertilizers, etc. The created method entails building a prototype that makes use of basic, affordable equipment including a microprocessor, different motors, and terminal equipment to assist farmers in a variety of operations related to crop fields. Design and build an autonomous drone-based surveillance system capable of identifying injured crops and spraying pesticides in specified regions as needed. To combine an image processing and Artificial Intelligence based real time algorithm to determine crop health and evaluate the need for pesticides, as well as a weather monitoring system that can assist anticipate weather conditions. [ABSTRACT FROM AUTHOR]

With millions of people segregating around the globe, Coronavirus stands truly a global event. It ranges to the trajectories of states with miserable and wrecked health care systems. The transmission is aided by the wide-ranging response from the policy planning and state organizations. Experts are aware of the sternness and contamination of the infectious disease and its disastrous consequences that desire for inoculation of Artificial Intelligence (AI). The absence of an AI policy rejoinder may lead to increased fatalities for weathering the storm. Despite the wide range of responses, the up-to-date policy needs an organized way to track the inflexibility of state-run organizations' frameworks to attain the objectives of AI organizational policy response. The study's objectives include including key national institutions to understand perceptions and motivations to challenge the event of COVID-19 through common grounds of Artificial Intelligence. The data is obtained through an online survey from the foreign office, health care services, inter-coordination ministries, and science and technology ministry. The paper has unfolded the useless directions, impractical steps, uncertainty, ineffective communication, and social protection, which led to the rapid spread of infection. Refining each health indicator and reducing the progression of the pandemic through the AI archetype is conceivable only when officialdoms employ the AI-based approach. [ABSTRACT FROM AUTHOR]

Semantic analysis is a particular technique, which is an interesting area of research that associates with Natural Language Processing (NLP), artificial intelligence, opinion mining, text clustering, and classification. Numerous text processing techniques are being used to find out sentiments from the comments, such as social media tweets, hoax, fiction, nonfiction, novels, books, movies, health care, and stock exchange. Agrarian experts' opinions play a vital role in the agriculture sector that yields good crop productivity. This paper presents a descriptive analysis of agriculture experts' opinions through machine learning methods based on textual data collection. The data has been collected by surveying various academia, research institute, and industry of Punjab, Pakistan. The impact of various agricultural inputs such as seed quality, soil quality, soil-intensive tillage, climate changes, water shortage, synthetic fertilizer, and precision technologies on crop productivity have been collected through questionnaires. This research provides a descriptive analysis of collected agrarians experts opinions to increase the crop yield by providing awareness regarding current agriculture inputs to farmers by using machine learning. The current research provides a cohesive expert guideline for improving crop productivity, useful for agricultural policymaking, and conveys adequate farmers' knowledge. Consequently, the proposed method is an innovative way of discovering recommendations of agrarians through sentiment analysis in survey data using machine learning methods. Furthermore, to the best of our knowledge, agrarians experts opinions on enhancing crop productivity have been considered for the first time in Pakistan. [ABSTRACT FROM AUTHOR]

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches. [ABSTRACT FROM AUTHOR]

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques. [ABSTRACT FROM AUTHOR]

Motion sign-based language has an important role in the mute community, that is for data transmission. Usually, silent and dumb people counter very difficult situations to convey information to normal people. This paper proposes research that can ease the life of the deaf community. The work presented in this paper is a communication bridge between normal-hearing persons and persons with less hearing ability or impaired persons. The proposed research 'Gesture-Talk' can be used as an interpreter between any normal person and deaf and mute persons. The Gesture-Talk is based on the language used in Pakistan which is Pakistan Sign Language (PSL), which is a standard language used by deaf persons in Pakistan. Using Gesture-Talk, PSL can be translated into voice. The idea is to develop a small portable application that can be used as a middle layer application between normal and deaf people. The authors have used an electrical sensor that will collect the data by detecting electrical pulses that will be sent to the microcomputer (raspberry pi 3) where the data will be processed and sent to the speaker from which voice will be generated. A cost-effective and novel approach has been proposed in this research paper compared to the other existing approaches. Moreover, Artificial Intelligence (AI) based predictive or classification algorithms may be applied for the best optimal results. [ABSTRACT FROM AUTHOR]

Electrical Power quality in distribution systems is crucial to both utility and consumers simultaneously. The main issue that affects the quality factor of electrical distribution systems is phase load imbalance. Phase imbalance is a major issue in distribution networks in Pakistan, India, the United States, China, and other nations and regions. The distribution system in Pakistan is normally a three-phase, four-wire system, whereas our residential and commercial loads are often single-phase, resulting in an unbalanced system. These unbalanced circumstances in the system result in single-phasing, overloading, and overheating situations, and the return of current to neutral, as well as increased power system investment and operational expenses. In this paper, several methodologies for phase balance are being studied. After researching several techniques of phase balancing and building on that methodology, a simulation prototype is developed, and different unbalanced situations are studied. To analyze unbalanced conditions in practical mode, a hardware prototype is developed on the basis of simulation. Examined some unbalanced loads on the simulation prototype and then on the hardware prototype and achieved the best possible load balancing on phases. [ABSTRACT FROM AUTHOR]

This paper attempts to explore many signs of progress enabled by Artificial Intelligence (AI) in financial and corporate business management. It also amid to identify the benefits and cons of AI applications in social life. A systematic content analysis approach has been used to demonstrate the developmental phases of AI. Four distinct organizational maturity clusters i.e. Pioneers, Investigators, Experimenters, and Passives have been developed on basis of dataset. Data collections was carried through emails, customizable chatbots, live chat softwares and automated helpers of top ten online companies and various banking and financial institutions located in Lahore and Karachi cities for making behavioral analysis. The data results revealed that all aspects of financial managements and corporate business activities have been highly influenced by the application of AI. The study demonstrated that 80% senior business executives were of view that AI boost productivity and creates new business avenues. The results also demonstrated that 88% Pioneer organizations have understand and adopted AI techniques according to organization requirements, 82% Investigator organizations are not using it beyond the pilot stage whereas 24% Experimental organizations were adopting AI without understanding it. These results seem to reflect that AI has profound effects on financial industry to streamline its credit decisions from quantitative trading to financial risk management and fraud detection. This study also discovered that the widespread use of AI have raised a number of ethical, moral and legal challenges that are yet to be addressed. Although AI is gaining popularity day by day and it is believed that AI will improve work performance beyond human standards but it could not replace human resources fully. [ABSTRACT FROM AUTHOR]

Several trends show that the efforts to alter South Asian strategic landscape have increased, which primarily includes unprecedented proliferation of technology to India. This has exacerbated Pakistan's security dilemma and is a matter of significant concern for South Asian security environment. Militaries worldwide are preparing for a new warfare trend in emerging technologies that include artificial intelligence and machine learning-based autonomous weapon systems, robotics, hypersonic weapons, and quantum technologies. These technologies are expected to change the character of war besides affecting geopolitical competitions and rivalries. It is pertinent to analyse the impact of these new technologies on regional rivalries to identify and implement effective solutions, thus mitigating potential risks and preventing the occurrence of a catastrophe. As several states are developing AI-based Lethal Autonomous Weapon Systems, capable of targeting without human supervision, possible exploitation of these technologies by nuclear-armed states may increase the risk of war leading to more aggressive nuclear postures. Such a risk would be higher in South Asia as the ballistic missile flight time would be less than ten minutes, and conventional military interactions of rival states could occur near a long border. The integration of autonomy into conventional weapons and nuclear systems has the potential to undermine strategic stability and will be a quick recipe for accidents and miscalculations. This study explores the region's threat perceptions and analyses the potential use of LAWS and how it can impact nuclear deterrence and strategic stability of South Asia. This study hypothesizes that LAWS will lower the nuclear threshold in the region and undermine deterrence stability. [ABSTRACT FROM AUTHOR]

The paper presents an application of Influence Nets (INs) in the field of financial informatics. Influence Nets have primarily been used in war games to model effects based operations but, as shown in this paper, they can prove to be equally useful in other domains requiring decision making under uncertain situations. The primary advantage of INs lies in their ability to acquire knowledge from subject matter experts in problem domains that rely heavily on experts' opinion. A sample case study from the fields of economics and finance is presented in this paper. The case study models the choices faced by a developing country to recover her economy which is going through a difficult phase due to global financial crisis, internal law and order situation and political instability. [ABSTRACT FROM AUTHOR]

This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity. [ABSTRACT FROM AUTHOR]

Information and Communication Technology (ICT) plays an immense role in reappraise and transform human life around the globe. In this digital era, not only men but women are also contributing potentially to the ICT field to end poverty, improve education, and a create decent job market. Hence, it is necessary to encourage women to pursue their careers in the ICT field for the development of the country. Consequently, it is mandatory to evaluate women's contributions to the ICT field. Thus, in this research, women's empowerment in the ICT field was analyzed based on women's designation, and qualification, a field of interest in all ICT institutes of Pakistan. For this purpose, data of women faculty of 124 ICT institutes of Pakistan was collected from online available resources of ICT institutes using web scraping technique. In total, the dataset was consisting of 1515 entries that were mined from different ICT institutes in Pakistan. The attributes included in this dataset were female names, designation, qualifications, area of Interest, department, university, and province. The paper is presenting the first step for performing the gender-based bibliometric analysis. This study is quite beneficial for evaluating Pakistani women's performance in ICT by analyzing their qualifications, designation, and department. It is concluded that most women faculty work in the Computer Science i.e., sub-field of ICT, and women are more interested in Data Science, Image processing, Artificial Intelligence, and Machine Learning research areas. [ABSTRACT FROM AUTHOR]

The focus of this research study was to evaluate the effect of the artificial intelligent technology and its use in graduate learners to improve English writing skills. A qualitative approach was employed to conduct the research. The sample of this study was comprised on 120 learners of English as a second language from four departments at the UMT (University of Management and Technology), Lahore. A semi structured interview was shared within the WhatsApp groups of students. The interview was comprised of five questions. The results of the study highlighted the positive impact of the use of AI-based writing tools on learners’ efficiency by providing instant feedback, reducing the time and effort required for proofreading and editing. The findings of this study will guide instructors and educators in adopting AI writing tools to enhance learning of English writing skills. [ABSTRACT FROM AUTHOR]

This paper focuses at Natural Language Processing technology research in Pakistan and India with specific focus on research groups working on Speech Processing Technology and initial efforts to enrich the linguistic database of regional languages in research institutions, universities and multinational organizations. Speech Processing Technology is a specialized research field and is increasingly becoming popular in areas of information communication technology, humanoid robots, artificial intelligence, and consumer services in banking, telecom sector, industries, health (speech assistance for the disabled) and education. It comprises Speech Comprehension Technology and Speech Generation Technology. For both these technologies to perform efficiently, reliable linguistic database is essential in targeted languages. This study highlights the potential projects of research groups and the strengths and weaknesses in this research area in Pakistan and India, and how both countries can benefit from each other's experience in speech processing research. There are striking similarities in the speech utterances of languages spoken in Pakistan and India like Sindhi, Kashmiri, Urdu, Hindi and Punjabi. In Pakistan, Natural Language Processing and Speech Processing Technology is at a very early stage as compared to India, which has initiated research projects in Sanskrit, Hindi and more than twenty regional languages. In both countries, there is a strong need to develop a large linguistic database of these languages before any further research in processing systems for speech comprehension or speech generation can be initiated while highlighting the issues related with lack of sustained research projects in Pakistan. This paper suggests areas of improvement in Pakistan by enriching the linguistic database in the indigenous Pakistani languages for market based research for improvement in consumer services in Speech Processing Technology in Pakistan. [ABSTRACT FROM AUTHOR]

Renewable energy sources have gained widespread attention due to their abundance and cost‐effectiveness. In particular, healthcare systems and hospitals are increasingly seeking clean and renewable energy solutions to reduce energy costs, price volatility, and protect community health. However, proper load estimation and techno‐economic analysis are crucial to ensure the optimal performance and reliable power supply of renewable energy systems. This study focuses on conducting a case study on load estimation and techno‐economic analysis for a hospital located in a remote area of Azad Jammu and Kashmir, Pakistan. Through this analysis, the study aims to determine the feasibility of implementing a renewable energy system at the hospital and assess the potential benefits in terms of energy production, cost savings, and environmental impact. Two different clean energy management softwares, HOMER PRO and RETScreen Expert, are utilized to compare the proposed hybrid (PV/Wind/Battery/Convertor/Grid) system with the existing system in terms of cost minimization, optimal system configuration, and environmental effects. The results demonstrate that the proposed hybrid system is more efficient, stable, cost‐effective, and a environmentally friendly option. Moreover, the annualized energy production, consumption, and grid load reduction are also calculated. The findings of both software programs are compared for validation of the proposed optimal system. The techno‐economic analysis of the proposed system can serve as a useful guideline for other commercial facilities in the region to utilize solar and wind energy. [ABSTRACT FROM AUTHOR]

The article aims to explored turn repair strategies by the speakers during a conversation. Turn taking is an important aspect of conversation analysis research. Focus of this study is limited to self-repair strategies used by the speakers during conversation. Data for the study is as interview by Joseph Downing, an American painter. The study follows mix method research that is a combination of quantitative and qualitative analysis. Theoretical background of the study is Zsuzsanna Németh's (2012) idea of self-repair strategies used by the speakers to achieve their interactional goals. The results prove that the speakers use STSR strategies of recycling, restating and replacing while they are involved in interviews and they are facilitated by listeners to use these strategies and to make their speech clear and correct. The study recommends the analysis of self-repair strategies in other conversational context for discovering the socio-cultural aspects behind the repair of the speech. [ABSTRACT FROM AUTHOR]

Skills are essential for people with exceptional needs. They are able to uncover their natural talent. Pre-vocational services may be provided to people with IDDs in an effort to prepare them for the workforce before they enter the broad field of vocational skill training (Kirono, 2013). The objective of the existing research was to explore the needs of the Pre-vocational skills of the Persons with Mild Intellectual and Developmental Disabilities (MIDDs. Philosophical research paradigm of current study was quantitative. Data were composed through a structured survey questionnaire with five points multiple directional scale for parents of the persons with MIDDs. Sample of the study was consisted of 151 parents of MIDDs studying in Government Special Education and training institutes of Punjab. Major findings of the research showed that functional academic skills were assessed to be the most problematic pre-vocational skills of MIDDs. The study recommended that persons with MIDDs should be taught according to the individualized educational program. [ABSTRACT FROM AUTHOR]

This Regular Feature explores the role of health science librarians in the coronavirus pandemic. COVID‐19 has spread rapidly all over the world. All major cities around the globe are in lockdown. In Pakistan, the first case was diagnosed on 26 February 2020, and currently, there are more than 2039 diagnosed cases and 26 deaths as on 31 March 2020. Across the country, there are further 12 000 suspected cases. This will undoubtedly increase if precautionary measures are not taken. Pakistani universities, colleges and schools were in lockdown. The role of university librarians in this emergency included raising awareness through public health education, providing support to medical staff, researchers and providing ongoing traditional services to regular library patrons in Pakistan. The Regular Feature also provides links to useful resources. JM. [ABSTRACT FROM AUTHOR]

Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Sexually Transmitted Diseases (STDs) constitute important primary health issues in Pakistan which face inadequacy of resources required in early detection and investigative procedures for their diagnosis and treatment. Syndromic approach to management of STDs is based on the identification of a consistent group of symptoms and syndromes to classify the exact disease or infection beforehand, so that further investigations are sought for based on these initial criteria. This paper envisions the results based on two different approaches: Human and Artificial Intelligence (AI) along with some examples of on-going usage e of Artificial Intelligence in Medicine. Pakistan is in an early stage regarding the use of informatics in health care for sustainable health system but is also under international obligation to adapt it & WHO EMRO has developed an e-Health plan for the member countries including Pakistan. The paper presents an informatics application model for a very common but important problem & development of such e-health applications, as starting point, will certainly have positive impact the future of development of e-Health in Pakistan.

Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed of all vehicles on roads at all spatial points. They can only detect the speed of the vehicle the line of sight of the camera. A solution is to deploy a huge number of speed guns at different locations on the road to detect and report vehicles that are over speeding. However, this solution is not feasible because it demands a large amount of equipment and computational resources to process such a big amount of data. In this paper, a speed detection framework is developed to detect vehicles' speeds with only two speed guns, which can report speed even when the vehicle is not within the camera's line of sight. The system is specifically designed for an irregular traffic scenario such as that of Pakistan, where it is inconvenient to install conventional systems. The idea is to calculate the average speed of vehicles traveling in a specific region, for instance, between two spatial points. A low-cost Raspberry Pi (RPi) module and an ordinary camera are deployed to detect the registration numbers on vehicle license plates. This hardware presents a more stable system since it is powered by a low consumption Raspberry Pi that can operate for hours without crashing or malfunctioning. More specifically, the entrance and exit locations and the time taken to get from one point to another are recorded. An automatic alert to traffic authorities is generated when a driver is over speeding. A detailed explanation of the hardware prototype and the algorithms is given, along with the setup configurations of the hardware prototype, the website, and the mobile device applications. [ABSTRACT FROM AUTHOR]

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors' nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable. [ABSTRACT FROM AUTHOR]

Following social cognitive theory, the current study investigated the impact of artificial intelligence (AI) on employees' productivity in the healthcare sector. AI significantly facilitates the management of hospitals to vigilantly assess employees' productivity and accurately analyze employees' characteristics, such as attitude, emotion and behavior. With the underlying mechanism of employee mental health and well‐being, and knowledge sharing, the study has considered beneficial and harmful perspectives of AI in the workplace. The study also hypothesizes the important moderating role of technological leadership. The data was collected from 184 doctors in Pakistan's major hospitals. Partial least squares (PLS) results support a direct relationship between AI and employee productivity. The findings also supported the underlying mechanism of knowledge sharing and mental health and well‐being in the relationship between AI and employee productivity. However, the technological leadership moderating effect was found to be insignificant. It opens an important avenue for this further research and future directions. [ABSTRACT FROM AUTHOR]

Background: This article analyzes scientific sources on the process of digitalization in the reverse supply chain. Its aim is to comprehensively investigate and analyze the transformative potential of digitalization in the context of the reverse supply chain. By exploring the utilization of digital technologies such as the Internet of Things (IoT), data analytics, artificial intelligence (AI), and blockchain, the study aims to uncover opportunities for enhancing the efficiency, sustainability, and environmental responsibility of reverse supply chain processes. A significant number of studies on this topic have been published in scientific journals such as Sustainability, Business Strategy and the Environment and the International Journal Of Production Economics. The most cited authors were identified, including Gupta and Yu. Among the main countries where such research has been conducted are China, the United States, the United Kingdom, India and Pakistan. Methods: The study included a literature review, evaluation, analysis and mapping, which allowed the authors to identify certain trends. The Scopus database was used for this purpose, and the selected articles were analyzed using MS Excel and VOSviewer. Initially, 297 documents were identified, and 82 articles remained after exclusions. Results: The findings of the study emphasize the growing interest in this topic, the increasing number of related scientific publications, and the importance of the sustainable use of resources in the reverse supply chain. Conclusions: The relevance of this study lies in the possibility of optimizing the processes of the reverse supply chain, ensuring the rational use of resources and achieving sustainable development. The application of the results obtained could be useful for a wide range of industries, including the activities of enterprises and the formation of policies for the management and development of economic sectors. [ABSTRACT FROM AUTHOR]