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Ravi Ramos (gaming SW engineer in Ubisoft, expert in ML/AI and Bioinformatics) speaks about trends, possibilities and limits of AI/ML and data science with Erik Ferencz (Google Cloud Developer, full-stack engineer, Revolgy). Ravi shares his impressive professional experience in the gaming industry, cloud technology, science and the way from Brazil to Brno, Czechia. Ravi is a real expert in ML/AI, so enjoy technical deep dive!

Advocates of artificial intelligence and machine learning in drug repurposing claim that it will increase efficiency, speed development, decrease costs, and protect patient welfare. In the following, we will use systematic review and meta-analytic methods to empirically evaluate the impact and outcomes for drugs and biologicals that have been repurposed as therapy for Covid-19 on the basis of artificial intelligence or machine learning hypotheses. For a cohort of drug repurposing hypotheses that entered the literature between 2020-01-01 and 2020-04-30, we will measure impact in terms of FDA approvals, FDA Emergency Use Authorizations, recommendation in NIH Covid-19 Treatment Guidelines, and positive RCT result within 6 months of follow-up after the first published recommendation of the drug repurposing hypothesis. We will measure trial outcomes in terms of number of trials, patient enrolment, meta-analyzed overall survival hazard ratios, and meta-analyzed proportions for treatment-related deaths and grade 3-4 treatment-related adverse events.

The integration of advanced technologies such as Artificial Intelligence (AI), encompassing machine learning (ML), deep learning, and neural networks, holds significant promise in revolutionizing patient care within outpatient primary clinics and ambulatory care settings. While the exploration of these technologies' implications for healthcare delivery is ongoing, several studies have delved into their potential benefits. AI-driven algorithms can analyze vast amounts of patient data, including electronic health records (EHRs), diagnostic images, and wearable device data, to assist healthcare providers in making more accurate diagnoses and treatment decisions. ML algorithms can predict patient outcomes, identify patterns in disease progression, and personalize treatment plans based on individual patient characteristics and preferences. Deep learning techniques excel in image recognition tasks, aiding in the interpretation of medical images such as X-rays, MRIs, and CT scans. Furthermore, AI-powered clinical decision support systems can augment healthcare professionals' decision-making processes by providing real-time recommendations and alerts regarding potential diagnoses, medication interactions, and treatment options. Despite these advancements, challenges remain in integrating AI technologies into outpatient primary clinics and ambulatory care settings. Issues such as data privacy and security, algorithm bias, interoperability of AI systems with existing healthcare infrastructure, and provider acceptance and adoption need to be addressed to realize the full potential of AI in improving patient care efficiency and effectiveness. In summary, while there has been examination and exploration of the integration of advanced technologies like AI, ML, deep learning, and neural networks in outpatient primary clinics and ambulatory care settings, further research and implementation efforts are needed to fully leverage their benefits and address associated challenges.

Scoping review with the objective of analyzing the regulatory framework for applications of Artificial Intelligence and Machine Learning in medicine in the member countries of Mercosur (Brazil, Argentina, Paraguay and Uruguay), identifying their main characteristics, convergences, differences and gaps.

In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.

This scoping review aims to systematically map the existing literature on artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) tools developed to generate multimodal (visual, textual, and audio) summaries of the intensive care unit (ICU) journey and discharge experience. Survivors of critical illness often experience fragmented transitions of care due to inadequate communication about their ICU stay. Traditional discharge summaries tend to be clinician-centred and predominantly text-based, often excluding contextual and patient-understandable information. In contrast, emerging AI-driven technologies offer the potential to automatically generate personalised summaries that combine narrative text, visual timelines, and audio explanations, improving continuity of care and patient understanding. Despite the rapid advancement of AI applications in healthcare, there is currently no comprehensive review examining how such multimodal summary tools have been applied or developed specifically for the ICU context. This review will identify and describe existing tools, their output modalities (textual, visual, audio), target users (clinicians, patients, families), and reported outcomes, limitations, and implementation factors. The findings will inform the development of future AI-based solutions for ICU survivorship and recovery communication. This review also forms a key evidence base for the Critical Illness Brain Dysfunction Survivorship Plus (CIBS+) programme and supports ongoing NIHR and Wellcome-funded research into personalised post-ICU digital health tools.

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Amii is one of the leading organizations in Artificial intelligence and machine learning. They have been assisting employers and businesses of all sizes in the disruptive field of Ai and Ml adoption. Learn how AI is changing the world of business. 

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This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfallsis a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.

Artificial intelligence and machine learning and data science has become what IT and ITES industry is used to be in the 1980s the scope of opportunities as a huge in these new emerging sectors--- This episode is sponsored by - Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/nirmit-verma/support

Addressing the critical gap in examining AI and ML applications and their strengths and limitations on system and organizational-level dimensions of PHC is essential for understanding how these technologies could impact core elements including health system governance resource allocation, workforce planning, and intersectoral coordination. As Health Services and Policy Research focuses on studying the structural and organizational elements underpinning health care systems, this scoping review aims to (i) map the current applications of AI and ML in meso- and macro-level elements of primary health care (PHC) within Health Services and Policy Research literature, (ii) assess the strengths and limitations of these tools, and (iii) identify application gaps and future directions in the field.

Artificial intelligence (AI) is an intelligence technology that is artificially programmed by humans to mimic humans. In clinical context, the use of AI and related technologies (e.g., machine learning and deep learning) are increasingly becoming popular. These technologies can aid clinicians in the diagnostic process and in the rehabilitation pathway, especially in disorder of consciousness (DoC), in which this aspect is particularly challenging. The aim of this scoping review is to map the literature on the role of AI, ML and DL in the field of diagnosis and rehabilitation treatment of DoCs, highlighting the current strengths and gaps, suggesting future perspectives. In particular, we tried to answer to the following questions: -What is the role of AI in the diagnosis and rehabilitation of DoCs? -What are the most used AI technologies in the field of DoCs? -How can AI improve patients’ care? -How reliable is AI in DoCs diagnosis compared to traditional diagnostic methods?

مدخل الى الذكاء الاصتناعي

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This scoping review aims to identify the application of clinical outcome prediction modeling based on artificial intelligence and machine learning using nursing data in intensive care units.

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Current Developments in the Field of Machine Learning and Artificial Intelligence 

Establishing early diagnosis for female adults using AI and ML is especially important since after the COVID-19 pandemic, as there was a noticeable decline in breast and cervical cancer screening of 87% and 84% in the United States with low-income women and women of color. This reduction in screening could lead to delayed diagnosis, later-stage identification of the cancer, and, unfortunately, worse outcomes, like death. A recent prospective study evaluated the use of AI and ML in cancer detection and false positive findings by looking at female adults with breast cancer. This study revealed that using AI significantly increased cancer detection rates while false positives declined compared with the standard of care, reducing unnecessary worry for many women. However, cancer screening within women continues to decline, even after COVID-19. There is a need to identify and understand the current circumstances of AI and ML's application on cancer screening and prevention among adults, primarily female adults, for more effective cancer care prevention and recommendations for the future.This scoping review aims to identify the barriers encountered in applying innovative AI, ML, and technological tools in promoting cancer screening among adult females. It will also address the role of patient-provider communication and trust in ensuring the success of AI applications and integration in primary care and prevention efforts. This scoping review will inform and guide future global recommendations for AI application in cancer prevention and care for female populations.

Systematic and critical evidence syntheses remain essential for clinical decision making. Indeed, there has been an explosion of available evidence in many fields making it challenging for systematic reviews to keep up. Novel tools and processes have been developed recently to automate systematic review steps using algorithms including artificial intelligence (AI) and machine learning (ML) (AI-ML). The objective of this project is to evaluate the performance and usability of previously published AI-ML tools by comparing their performance to that of “human reviewers” in the title and abstract screening phase of the systematic review process. The dataset generated by a large and highly cited Cochrane review will provide a testing ground for the AI-ML tools evaluated in this project. (1)

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Breast cancer remains one of the most prevalent cancers affecting women globally. Early and accurate diagnosis is critical for improving treatment outcomes and survival rates. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools in the field of medical diagnostics, particularly in oncology. These technologies have shown promise in enhancing diagnostic accuracy, reducing human error, and streamlining workflows in radiology, pathology, and genomic analysis. This systematic review aims to comprehensively evaluate existing literature on the application of AI and ML techniques in breast cancer diagnosis. The review will focus on assessing the diagnostic performance (e.g., sensitivity, specificity, accuracy) of these models across various diagnostic modalities such as mammography, ultrasound, MRI, histopathological images, and genetic biomarkers. The purpose of this review is to Synthesize evidence on how AI and ML are being utilized in breast cancer diagnosis, Identify which algorithms and datasets are most commonly used, Compare the performance of AI/ML models with conventional diagnostic methods, Highlight gaps in current research and provide recommendations for future studies, The expected outcomes include a detailed summary of current AI/ML technologies in breast cancer diagnostics, an evaluation of their diagnostic accuracy, and an identification of challenges and future opportunities for clinical implementation. This review will contribute to the body of knowledge that can inform clinicians, researchers, and healthcare policymakers about the readiness and reliability of AI-assisted diagnostic tools in breast cancer care.

This scoping review aims to provide insight on the usage of advanced ML and AI techniques in the design of clinical trials and acknowledge the gap between potential and actual applicability.

The objective of this review is to analyze the studies published to date on PK/PD mod-eling for antibiotic dose optimization using AI techniques. In addition, this review aims to evaluate in detail the different AI techniques used and the statistical metrics applied to assess the accuracy of the prediction and its comparison with the conven-tional monitoring techniques.

The objective of this review is to analyze the studies published to date on PK/PD mod-eling for antibiotic dose optimization using AI techniques. In addition, this review aims to evaluate in detail the different AI techniques used and the statistical metrics applied to assess the accuracy of the prediction and its comparison with the conven-tional monitoring techniques.

To explore the extent of actual use of AI/ ML protocols for diagnosing cancer in prospective (clinical trial or real-world) setting

To watch the remainder of the webinar follow the link: https://bit.ly/2IqUuEH AI and ML adoption in the enterprise is exploding from Silicon Valley to Wall Street. Ubuntu is becoming the premier platform for these ambitions — from developer workstations, to racks, to clouds and to the edge with smart connected IoT. But with new developer trends comes a plethora of new technologies and terminologies to understand. In this webinar, join Canonical’s Kubernetes Product Manager Carmine Rimi for: ∙ A decoding of key concepts in AI/ML ∙ A look into why AI applications and their development are reshaping company’s IT ∙ A deep dive into how enterprises are applying devops practices to their ML infrastructure and workflows ∙ An introduction to Canonical AI / ML portfolio from Ubuntu to the Canonical Distribution of Kubernetes and and how to get started quickly with your project And in addition, we’ll be answering some of these questions: ∙ What do Kubeflow, Tensorflow, Jupyter, Edge AI, and GPGPUs do? ∙ What’s the difference between AI and ML? ∙ What is an AI model? How do you train it? How do you develop / improve it? How do you execute it?

A scoping review which covers the different types or systems of Artificial intelligence and machine learning technologies and algorithms and its applications in various domains of Orthognathic surgery

[ Course Outline] Artificial Intelligence And Machine Learning ( 1)

In recent years, the role of Artificial Intelligence (AI) and Machine Learning (ML) in structural engineering has gained significant attention, particularly in the context of Structural Health Monitoring (SHM) and Predictive Maintenance (PM). These technologies are poised to revolutionize the way we monitor and maintain critical civil infrastructure, such as bridges, buildings, and dams, by offering enhanced capabilities for early failure detection, performance prediction, and maintenance optimization. As the demand for infrastructure grows globally and the risks associated with aging structures increase, traditional methods of inspection and maintenance are proving inadequate. The integration of AI and ML algorithms into SHM systems presents an opportunity to detect potential issues long before they develop into catastrophic failures, reduce maintenance costs, and extend the lifespan of civil infrastructure. This paper explores the transformative impact of AI and ML on SHM and PM in civil engineering, discussing their application in real-time monitoring, anomaly detection, and the optimization of maintenance schedules. The advent of smart sensors, IoT technology, and big data has allowed for the continuous collection of structural health data, including vibration, strain, displacement, and temperature, which can be analyzed through AI and ML algorithms to provide actionable insights into the condition of infrastructure. By processing vast amounts of sensor data, AI and ML can identify hidden patterns and correlations, enabling the creation of models that predict the future performance of structures under various conditions. Predictive analytics is a key component of these technologies, as it allows engineers to forecast the remaining useful life (RUL) of components and determine the optimal timing for repairs or replacements. Machine learning models, including supervised learning, unsupervised learning, and reinforcement learning, are being applied to large datasets of historical inspection and operational data, enabling accurate predictions of when and where failures are likely to occur. These models use algorithms that learn from previous observations to make data-driven predictions, thereby providing a proactive rather than reactive approach to maintenance. For instance, in the context of bridges, AI-driven systems can process data from sensors embedded in the structure to monitor changes in load, strain, and stress. ML algorithms can detect anomalies such as cracks, corrosion, or material fatigue, and provide real-time alerts to engineers. By combining these findings with weather data, traffic patterns, and historical maintenance records, predictive models can optimize maintenance schedules, ensuring that repairs are carried out at the most cost-effective times, avoiding costly downtime or emergency repairs. Similarly, AI-based systems have been successfully implemented in dam monitoring, where sensor networks track the movements of the dam structure, water pressure, and other environmental factors. By using ML to analyses the data, engineers can predict potential structural failures such as seepage or deformation, long before they manifest as visible damage, thus minimizing risk and enhancing safety. Another promising application of AI and ML in structural engineering is smart cities and smart infrastructure, where interconnected systems allow for continuous monitoring and real-time decision-making. In these environments, AI-powered systems can be integrated with broader urban management frameworks, providing insights into the health of various infrastructure elements (e.g., roads, buildings, tunnels), ensuring that resources are deployed efficiently and maintenance efforts are prioritized based on criticality. The potential benefits of AI and ML in SHM and PM are vast. Not only can these technologies help detect early signs of damage, but they also allow for more efficient use of resources, extending the lifespan of infrastructure while minimizing disruptions. However, their successful implementation comes with challenges, including the need for high-quality data, robust algorithms, and suitable sensor networks. Moreover, as infrastructure continues to grow and become more complex, the integration of AI and ML must be complemented by careful consideration of system interoperability, data privacy, and cybersecurity issues. This paper also examines case studies from around the world where AI and ML have been successfully integrated into structural health monitoring systems. These examples illustrate the practical applications and the potential of these technologies to provide more accurate, timely, and cost-effective solutions for infrastructure maintenance. By leveraging these advancements, engineers and policymakers can make informed decisions, reducing risks, improving safety, and ensuring the sustainable operation of critical infrastructure.

The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.

The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.

The evolution of new technologies has spurred a growing body of literature exploring their application and impact on the financial sector, particularly the integration of artificial intelligence (AI). This paper delves into the rapid adoption of AI and machine learning within the financial sector, highlighting their potential to enhance financial stability and productivity. By reviewing research from 2018 to 2023, the study categorizes AI applications in finance into three main areas: cybersecurity, customer services, and financial management. Furthermore, the research identifies and classifies various threats posed to the integrity and stability of the financial system by AI, along with associated challenges for policy and regulatory frameworks. It also addresses the risks and obstacles inherent in deploying AI within financial markets and banking sectors, offering recommended strategies to mitigate these limitations. Despite the recognized advantages, the comprehensive understanding of AI's benefits and drawbacks remains incomplete due to its evolving nature and varied applications in banking. Clear policies governing AI usage are imperative to safeguard financial consumers and promote a fair and transparent financial market. These guidelines should prioritize human decision-making and foster an unbiased approach to policymaking, ultimately fostering innovation within the industry.

Title: Applications of Artificial Intelligence and Machine Learning to Office Laryngoscopy: A Scoping Review Authors: Peter Yao Alexander German Katerina Andreadis Anaïs Rameau Contact: Peter Yao [email protected] Weill Cornell Medical College Review Questions: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging? Searches: We searched the databases of MEDLINE, EMBASE, COCHRANE, Web of Science, and IEEE Explore. We searched using a combination of database-specific subject headings and text words for the main concepts of artificial intelligence and laryngoscopy, laryngeal structures or laryngeal pathology. The search strategy was customized for each database. Types of study to be included: There are no restrictions on the methodology, the type of model developed, or publication date. Studies not written in English, were not peer reviewed, or were review papers were excluded. Reviews were excluded from the analysis, but we screened the reference lists of relevant reviews to identify potential eligible studies. Condition or domain being studied: Artificial intelligence models applied to laryngoscopic videos or images. Participants/population: Patients undergoing office laryngoscopies. Intervention: Machine learning and artificial intelligence algorithms applied to video laryngoscopy or laryngoscopy images. Comparator/control: Not applicable. Primary Outcome: Identify, appraise, and synthesize the applications of artificial intelligence to laryngoscopy including by not limited to identification, detection, and segmentation of pathology, informative frame filtering, and anatomical segmentation. Additional outcome: This review will also identify existing gaps in the literature, and help formulate the best approach for applying artificial intelligence to laryngoscopic imaging to guide existing and future researchers. Data extraction: Screening: Literature search results were imported to Covidence, an Internet-based systematic review data management software that facilitates collaboration among reviewers during the study selection process. Articles were screened using a two-step process. In the first step, articles were screened by title and abstract by teams of two reviewers working independently. In the second step, articles that passed the title-abstract screen were screened by full-text by teams of two reviewers working independently. A third reviewer reviewed articles and resolved disagreements through consensus when the original two reviewers were not able to reach one. We will calculate the adjusted kappa statistic to measure interrater agreement for eligibility screening. Data Collection We divided the items within the data collection form into four blocks: (1) study information including publication year, author information, funding or sponsorship information, type of study, journal name and PICO elements; (2) database information including data source and sample size; (3) patient demographic information including gender, age, race, and disease diagnosis; (4) ML methodological information including ML model name, type, task, data classes, class split, how ground-truth labels are determined, objective function, and model performance. Data Synthesis: A systematic narrative synthesis will be provided with information presented in the text and tables to summarize and explain the characteristics and findings of the included studies. Analysis of subgroups or subsets: We will analyze studies in groups based on their objectives. For example, studies focused on diagnosis will be analyzed separately from studies focused on anatomical segmentation.

Unit 42 leaders Ryan Olson and Rick Howard present another episode of their \"Don't Panic\" podcast, where they break down the big issues in cyber security and tell you why you don't need to panic.Artificial Intelligence and Machine Learning are littered throughout cybersecurity literature, but just what exactly do these buzzwords mean? This episode uncovers the mysteries surrounding AI and Machine Learning to fully unpack these confusing concepts.

This is the review protocol used to conduct a scoping review on the use of artificial intelligence (AI) and machine learning (ML) in treatng Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD).

This forum convened experts in the AI and machine learning fields to discuss the future of these technologies and their implications for the communications marketplace. The event also included demonstrations to enable the public to see these emerging technologies in action. Held on November 30, 2018 at FCC Headquarters in Washington, DC. For more information, see https://www.fcc.gov/news-events/events/2018/11/forum-artificial-intelligence-and-machine-learning

Artificial intelligence and machine learning 

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A scoping review that aims to answer the question: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging?

Cancer immunotherapy has revolutionized cancer treatment by leveraging the immune system to target malignant cells, yet resistance in many cancers highlights the need for novel therapeutic targets. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools for identifying new immunotherapy targets by analyzing vast datasets from genomics, proteomics, and clinical studies. This review explores the role of AI and ML in advancing the discovery of cancer-specific immunotherapy targets, such as tumor antigens and immune pathways. Key advances include the integration of big data, neoantigen prediction, biomarker discovery, and single-cell analysis. Despite these advancements, challenges remain, including data quality and standardization, interpretability of AI models, computational costs, and the need for biological validation of AI-driven discoveries. As AI and ML technologies continue to evolve, they hold the potential to overcome these barriers, leading to personalized immunotherapy solutions. This review also discusses future directions for AI-driven immunotherapy, emphasizing the need for improved models, ethical considerations, and clinical integration. 

As a discipline, anesthesiology is well placed to benefit from applications of machine learning (ML) and artificial intelligence (AI). For clinical care and more specicifically pain management, perioperative management, drug discovery and intensive care, we seek to form a succint mapping study conducted in a systematic manner. The study will include either ongoing or completed records of anesthesiology research/practice/applications.

Artificial Intelligence (AI) can revolutionize agriculture which impacts a country’s economy, employing more than 30% of the world’s population directly or indirectly. It can fulfill the needs of an ever-growing world’s population through automation. Traditional farmland practices like weeding, pesticide spraying, irrigation, monitoring soil nutritional and moisture status, etc. can be performed quicker using robots, sensors, drones, and algorithms. It reduces water wastage and pesticide overuse, maintains soil fertility, helps in reducing labor and enhances crop yield and productivity despite world problems. However, its penetration into agriculture is still in its infancy due to its uneconomical nature, lack of expertise and big data requirement for accuracy among others. This paper delves deeper into the various applications and impacts of AI in agriculture, new tools being used, challenges and future scope related to this field. Combined with Artificial Neural Network (ANN) models and Machine Learning (ML), along with Expert systems (ES) and Internet of Things (IoT), AI can do wonders in agriculture in the subsequent years to come.

The swift progression of artificial intelligence (AI) technologies has significantly impacted various industries, with e-commerce platforms standing out as key beneficiaries. This paper delves into how AI enhances e-service quality on these platforms, emphasizing contemporary applications and emerging trends. AI driven tools such as chatbots, personalized recommendation systems, and predictive analytics have enabled e-commerce platforms to deliver more tailored, efficient, and satisfactory customer experiences. AI chatbots, utilizing natural language processing (NLP) and machine learning (ML), offer instant, round the-clock customer support, effectively resolving queries with minimal human intervention. Personalized recommendation systems employ sophisticated algorithms to analyse user behaviour and preferences, resulting in highly engaging product suggestions that drive customer engagement and increase sales. Furthermore, predictive analytics allow e-commerce platforms to forecast market trends, optimize inventory management, and tailor marketing strategies, thereby improving operational efficiency and customer satisfaction. This paper also examines the challenges of AI implementation, such as data privacy issues, the need for significant initial investments, and the necessity for ongoing technological updates. Through a thorough analysis of current AI applications and a review of recent scholarly works, this paper aims to underscore the transformative effects of AI on e-commerce service quality and provide insights into future advancements in this evolving field.

Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

Antimicrobial stewardship programs (ASPs) play a crucial role in promoting the appropriate use of antibiotics to combat antimicrobial resistance. ASPs encompass various strategies, including prospective audit with feedback, local guidelines implementation, education, and among others. However, implementing and maintaining ASPs can be resource-intensive, limiting their effectiveness and creating challenges for optimal ASP operations. Recent advancements in artificial intelligence (AI) and machine learning (ML) present promising opportunities to enhance the effectiveness of ASPs. Despite the potential benefits, there is a knowledge gap regarding the use of AI and ML in ASPs. Although some studies have shown positive outcomes using AI and ML in various healthcare applications, their specific impact on ASPs remains underexplored. This review aims to address this gap by providing an overview of the current state of research on AI and ML in ASPs, thereby informing actionable applications and guiding future research.

MoDOT Research Summary: Data Acquisition and Processing Using Artificial Intelligence and Machine Learning, July 2024; Prepared by MoDOT construction, Materials, and Research, Missouri Department of Transportation; MODOT; transportation planning; Project TR202215;

The requirement for talent to work in the field of artificial intelligence and machine learning is rapidly increasing. Our Certification in Artificial Intelligence and Machine Learning in collaboration with E&ICT Academy, IIT Guwahati is an industry-specific program designed by E&ICT Academy, IIT Guwahati and Imarticus Learning. This program adopts a very collaborative approach that focuses more on real industry projects and assignments, helps you prepare for interview opportunities through our career services and gives you the opportunity to create your own GitHub project portfolio.

Assessing the feasibility and acceptability of the AI and machine learning in this area can help to understand the opportunities and possible barriers for the implementation and further provide evidence to support the real-world practice. To our knowledge, no study has reviewed and synthesized the current evidence on this topic. Therefore, this scoping review aims to systematically locate and review literature on the feasibility and acceptability of AI and machine learning applications in health care for anxiety-related disorders. This will enable the identification of research gaps in this topic and then contribute to improving future research and supporting evidence-based real-world practice of AI and machine learning in mental health care.