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Large-scale automated systems such as manufacturing systems, transportation systems, the Smart Grid and many others are continuously becoming larger, more distributed, more complex, and more intelligent. There is a growing expectation that their software controller will make real-time intelligent decisions, at all levels of the control hierarchy that make up the enterprise. The need is changing for distributed intelligent controllers that are scalable to arbitrarily large systems. In this paper, we first present the model explosion problem. This problem arises when every controller in the control hierarchy is to have a unique simulation model of its unique control domain to use in its decision-making process. That is, the modeling effort needed to provide intelligence to all controllers in the control hierarchy grows exponentially with the number of controllers in the hierarchy using current modeling technology. Since each controller is in a unique location within the control hierarchy, each will need to have its simulation model custom made for its unique control domain, leading to the scalability issue that we refer to as the model explosion problem. Next, a new modeling paradigm that solves the scalability issue resulting from the model explosion problem is presented, where the simulation models are automatically generated by recycling the models used for control. If the controller models are created using the presented modeling paradigm, then these same models can be used for simulation with no modification or the need to understand the control logic. Furthermore, gathering the state from the physical system being controlled to initialize the simulation models in a real-time control application becomes a trivial operation of simply coping data from one software model to its identical copy, without the need to interpret the meaning of the data. Finally, an example of a hierarchical controller to control a small physical model of a manufacturing plant is presented. We show how we automatically generated all the simulation models in the control hierarchy without any modification and with minimal effort, and used them to make intelligent decisions in real time. [ABSTRACT FROM AUTHOR]

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems. [ABSTRACT FROM AUTHOR]

Successful performance in competitive swimming requires a swimmer to maximise propulsion and minimise drag, which can be assessed using instantaneous swimming velocity. Many systems exist to quantify velocity, and therefore, it is important to understand the agreement between systems. This study examined the agreement between an automated video-based system and a tethered system to measure instantaneous velocity. Twenty-two competitive swimmers (state level or higher) completed 25 m of each stroke at maximal intensity. The tethered speedometer was attached to the swimmer’s waist, while videos of each trial were recorded. The swimmer’s head was then automatically tracked using proprietary software, and instantaneous velocity was determined from each system. Bland-Altman plots showed good agreement between the two systems in backstroke (95% Limits of Agreement (LOA): −0.24–0.26 m.s−1) and freestyle (95% LOA: −0.36–0.38 m.s−1) but poorer agreement in butterfly (95% LOA: −0.51–0.53 m.s−1) and breaststroke (95% LOA: −0.88–0.92 m.s−1). The root mean square error was higher in butterfly (0.27 m.s−1) and breaststroke (0.46 m.s−1) compared to backstroke (0.13 m.s−1) and freestyle (0.19 m.s−1). Results demonstrated that the two systems are comparable for measuring instantaneous swimming velocity; however, larger discrepancies are evident for butterfly and breaststroke. [ABSTRACT FROM AUTHOR]

This article presents the proper organization of the supply chain to meet consumer demand, which is crucial for modern commercial enterprises involved in the sale of various products. Studies indicate that a company's success is linked to the satisfaction of its customers. To optimize the supply chain, this study will consider the use of artificial neural network models. The results of this model will seek a balance between demand and supply, helping determine the necessary quantity of goods to satisfy demand and prevent overproduction. By using this model, the company can fully meet the needs of its customers. Additionally, the company saves its resources and labor costs and reallocates them to other tasks. The model demonstrates the optimization of production and supply business processes, as well as an increase in efficiency. [ABSTRACT FROM AUTHOR]

Large-scale automated systems such as manufacturing systems, transportation systems, the Smart Grid and many others are continuously becoming larger, more distributed, more complex, and more intelligent. There is a growing expectation that their software controller will make real-time intelligent decisions, at all levels of the control hierarchy that make up the enterprise. The need is changing for distributed intelligent controllers that are scalable to arbitrarily large systems. In this paper, we first present the model explosion problem. This problem arises when every controller in the control hierarchy is to have a unique simulation model of its unique control domain to use in its decision-making process. That is, the modeling effort needed to provide intelligence to all controllers in the control hierarchy grows exponentially with the number of controllers in the hierarchy using current modeling technology. Since each controller is in a unique location within the control hierarchy, each will need to have its simulation model custom made for its unique control domain, leading to the scalability issue that we refer to as the model explosion problem. Next, a new modeling paradigm that solves the scalability issue resulting from the model explosion problem is presented, where the simulation models are automatically generated by recycling the models used for control. If the controller models are created using the presented modeling paradigm, then these same models can be used for simulation with no modification or the need to understand the control logic. Furthermore, gathering the state from the physical system being controlled to initialize the simulation models in a real-time control application becomes a trivial operation of simply coping data from one software model to its identical copy, without the need to interpret the meaning of the data. Finally, an example of a hierarchical controller to control a small physical model of a manufacturing plant is presented. We show how we automatically generated all the simulation models in the control hierarchy without any modification and with minimal effort, and used them to make intelligent decisions in real time.

Abstract The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

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The development of smart and innovative software applications in various disciplines has inspired our lives by providing various cutting-edge technologies spanning from online to smart and efficient systems. The proliferation of innovative internet-enabled tools has transformed the nation into a globalized world where individuals can participate on various platforms, collaborate in activities, communicate on issues, and exchange information safely and consistently. Coordination and cooperation are essential in software development. It gathers all software developers in one space, encouraging them to discuss goals and work rationally to accomplish the project goal. In recent years, continuous software development and deployment have become increasingly common in software engineering. Continuous software engineering (CSE) is a method that involves a variety of strategies to increase the regularity of novel and modified software versions. CSE enables a continuous learning and improvement process through rapid software update iteration by combining continuous integration and delivery. Continuous integration is a method that has arisen in order to remove gaps between development and deployment. Software engineers must handle uncertainty and alter stakeholders' requirements, which is possible through continuous software developmental strategies that manage the overall software cycle and produce high-quality software applications. The proposed study is a systematic review related to continuous software development and deployment and focuses on achieving four aims: (1) To explore the impacts of continuous development on software, (2) to pinpoint various tools used to carry out this process, (3) to highlight the challenges faced in adopting continuous approaches for development and (4) to analyze the phases of continuous software engineering. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of Support Vector Machines (SVM) in developing automated systems to detect hate speech on social media platforms, addressing a critical need for scalable solutions to enhance online safety and societal cohesion. By leveraging SVM's proven efficacy in managing high-dimensional data and optimizing the balance between precision and recall, the study offers a comprehensive methodology that includes data collection, preprocessing, model training, deployment, and evaluation. The results demonstrate robust average performance across key metrics, affirming the model's reliability in accurately identifying hate speech while minimizing false positives. This research advances the field by showcasing the practical and theoretical contributions of SVM in automated hate speech detection, highlighting its potential to significantly improve content moderation practices. The findings underscore the necessity for ongoing refinement of detection systems and collaborative efforts among researchers, technology firms, and policymakers to create more inclusive online environments that promote respectful discourse and community well-being.

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

The article highlights the issue of forming and managing customer loyalty in the service sector by using digital tools. The importance of customer loyalty as one of the most powerful components of an enterprise’s intellectual capital is emphasized. The types of loyalty are characterized according to the criteria of attitude to the brand (positive/negative) and the frequency of repeated transactions (absolute, hidden, false, zero). Loyalty indicators and how to calculate them are covered: customer retention rate, customer long-term value, Net Promoter Score, customer satisfaction surveys, customer effort evaluation, cause and effect questions, repeated purchases and referrals, engagement. It was noted that among the various types of loyalty (bonus program, fixed discount, temporary discount, multi-level discount program, product as a gift, partner loyalty program, paid loyalty program) the non-commercial program deserves the most attention, because in the long term it forms and testifies to absolute customer loyalty. The main types of loyalty management in the context of digitalization include transactional, social-network, related to the influence on engagement, emotions, consumer behavior, and intentions to recommend a service. It is emphasized that automated systems, such as software for managing business processes and online services for booking procedures, are becoming an integral part of modern beauty salons and offer a wide range of possibilities: online booking for services through a website or mobile application, reminders about visits by messages or e-mail; a calendar of appointments for services with the possibility of synchronization with personal calendars of beauty masters; automatic control of consumables balances, formation of supply orders, cash book management, income and expense accounting, profitability analysis; newsletters about promotions and special offers, maintenance of pages in social networks; storage of information about customers, their preferences, history of visits through the CRM system. The positive consequences of using automation in beauty salons include increasing efficiency and productivity, improving customer service, increasing profitability, expanding marketing and advertising opportunities, improving ratings and reviews, but among the negative ones, we single out dependence on technology, loss of personal contact, and threats to data privacy. A developed set of advertising events with seasonal specialization using online tools (conducting master classes and virtual consultations, seasonal loyalty programs, charity initiatives, promotions for special dates) is presented.

Wei-Yao Wang,1,2 Yi-Hsin Chen,3– 5 Yu-Lin Lee,1,2 Chen-Feng Chiu,6 Shih-Ming Tsao1,2 1School of Medicine, Chung Shan Medical University, Taichung, Taiwan; 2Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan; 3Department of Nephrology, Taichung Tzu Chi Hospital, Taichung, Taiwan; 4School of Medicine, Tzu Chi University, Hualien, Taiwan; 5Department of Artificial Intelligence and Data Science, National Chung Hsing University, Taichung, Taiwan; 6Department of Internal Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, TaiwanCorrespondence: Shih-Ming Tsao, Department of Internal Medicine, Chung Shan Medical University Hospital, &num; 110, Section 1, Jianguo North Road, Taichung City, 40201, Taiwan, Tel +886 4 24739595 ext.34718, Fax +886-4-23248172, Email tsmhwy@ms24.hinet.netBackground: Determining oxacillin susceptibility using reference methods and automated systems is crucial for treating invasive infections caused by Staphylococcus aureus. This study compares the oxacillin susceptibility results from the two automated systems with agar dilution and correlates them with genotypes of invasive S. aureus.Methods: Non-duplicate S. aureus invasive isolates were collected over an 11-year period. The oxacillin susceptibility was determined with Phoenix 100 (Jan 2011 to Aug 2018) or Vitek 2 (Sep 2018 to Dec 2021), and susceptibility for oxacillin and cefoxitin was determined with agar dilution. Methicillin-resistant S. aureus (MRSA) was confirmed with mecA existence, and the genotype was determined using SCCmec. The association between genotype and antibiotic susceptibility using two automated systems and agar dilution was evaluated.Results: A total of 842 invasive S. aureus, including 443 mecA+ MRSA and 399 mecA- MSSA, were collected. The susceptibility rates of oxacillin determined by two automated systems and agar dilution were 68.8% (76.8% for Phoenix 100 and 57.6% for Vitek 2) and 54.0%, respectively. When compared with the oxacillin susceptibility using agar dilution, the categorical agreement for Phoenix 100 and Vitek 2 were 0.46% and 0.88%, respectively (p < 0.001). One hundred and forty-three isolates were misinterpreted as oxacillin-susceptible S. aureus (OSSA) using automated systems while comparing with agar dilution, among which molecularly community-associated MRSA (CA-MRSA) outnumbered healthcare-associated MRSA (HA-MRSA) (99 vs 34, p < 0.001). There were 70 mecA+ OSSA (OS-MRSA) using agar dilution, among which 42 harbored SCCmec types were predominantly categorized as CA-MRSA (38, p < 0.001).Conclusion: The categorical agreement of Vitek 2 in determining oxacillin susceptibility and predicting mecA existence is comparable with agar dilution, whereas Phoenix 100 is not. Most of those ORSA determined by agar dilution but misinterpreted as OSSA by automated systems and OS-MRSA are categorized as CA-MRSA.Keywords: oxacillin susceptibility, automated systems, agar dilution, SCCmec, CA-MRSA

AbstractThe integration of Artificial Intelligence (AI) technologies in the field of education has prompted significant advancements, particularly in the domain of assessment and grading. This research delves into the potential of large language models, specifically OpenAI's ChatGPT, in simulating human-like interactions and accurately grading student assessments. To accomplish its objectives, the study compares the grading performance of ChatGPT with that of human correctors in a sample of second-year university students. The research findings indicate only a moderate correlation between the grades assigned by ChatGPT and those of human correctors, suggesting nuanced capabilities in providing comprehensive feedback and streamlining the grading process. While the study highlights the benefits of AI integration in education, it also addresses potential risks, including the exacerbation of educational inequalities and the limitations associated with AI's automated nature. This research contributes to the ongoing discourse surrounding AI's role in education, emphasizing the importance of striking a balance between AI and human instruction for optimal educational outcomes. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop an approach to planning the trajectory of the manipulator robot using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of manipulator robots and the review of intelligent control systems made it possible to obtain a complete picture of the current state of this issue. A system is proposed that can perceive the environment and control the movement of the robot by generating the correct control commands. For this, 3 tasks were solved, namely: analysis of the environment in order to determine its features, determination of the trajectory in order to neutralize the collision and determination of controlled influences for the executive authorities in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks are proposed. [ABSTRACT FROM AUTHOR]

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards. [ABSTRACT FROM AUTHOR]

Abstract In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets.

In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets. [ABSTRACT FROM AUTHOR]

The article highlights the issue of forming and managing customer loyalty in the service sector by using digital tools. The importance of customer loyalty as one of the most powerful components of an enterprise’s intellectual capital is emphasized. The types of loyalty are characterized according to the criteria of attitude to the brand (positive/negative) and the frequency of repeated transactions (absolute, hidden, false, zero). Loyalty indicators and how to calculate them are covered: customer retention rate, customer long-term value, Net Promoter Score, customer satisfaction surveys, customer effort evaluation, cause and effect questions, repeated purchases and referrals, engagement. It was noted that among the various types of loyalty (bonus program, fixed discount, temporary discount, multilevel discount program, product as a gift, partner loyalty program, paid loyalty program) the non-commercial program deserves the most attention, because in the long term it forms and testifies to absolute customer loyalty. The main types of loyalty management in the context of digitalization include transactional, social-network, related to the influence on engagement, emotions, consumer behavior, and intentions to recommend a service. It is emphasized that automated systems, such as software for managing business processes and online services for booking procedures, are becoming an integral part of modern beauty salons and offer a wide range of possibilities: online booking for services through a website or mobile application, reminders about visits by messages or e-mail; a calendar of appointments for services with the possibility of synchronization with personal calendars of beauty masters; automatic control of consumables balances, formation of supply orders, cash book management, income and expense accounting, profitability analysis; newsletters about promotions and special offers, maintenance of pages in social networks; storage of information about customers, their preferences, history of visits through the CRM system. The positive consequences of using automation in beauty salons include increasing efficiency and productivity, improving customer service, increasing profitability, expanding marketing and advertising opportunities, improving ratings and reviews, but among the negative ones, we single out dependence on technology, loss of personal contact, and threats to data privacy. A developed set of advertising events with seasonal specialization using online tools (conducting master classes and virtual consultations, seasonal loyalty programs, charity initiatives, promotions for special dates) is presented. [ABSTRACT FROM AUTHOR]

Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research., (© 2024. The Author(s).)

Triggered by events such as the murder of George Floyd, the last few years have witnessed the revival of the Black Lives Matter movement, the #metoo movement and other collective initiatives that support LGBTQ + communities and other historically disadvantaged groups. At the same time, the widespread and still growing capabilities of automated systems for decision-making – such as artificial intelligence (AI) and the more recent generative AI (GAI) – have surfaced a multitude of DEI (diversity, equity and inclusion) issues in various organizational and social settings. In this editorial, we lay out ideas on how the IS community can react, make an important difference, and offer substantial theoretical and practical contributions to social justice. We also provide guidance to prospective JSIS authors whose research concerns the consideration of DEI issues in the strategic design, deployment and use in practice of information systems with embedded emerging, automated, algorithmic-based technologies. [ABSTRACT FROM AUTHOR]