Cognitive Computing Architectures for Machine (Deep) Learning at Scale

Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security

_ For decades, artificial lift optimization has relied heavily on manual intervention, periodic well tests, and the judgment of experienced engineers. Electric submersible pumps (ESPs), gas lift, and beam pumps (the three most widely used lift systems) each have presented their own optimization challenges, such as balancing set points, managing compressor constraints, and interpreting dynamometer cards. These processes, while effective, have historically been time-intensive, reactive, and difficult to scale across large well inventories. The accelerating deployment of machine learning (ML) and automation is changing this landscape. By embedding intelligence into the control loop, operators now can move from reactive decision-making to proactive, continuous optimization. What was once a manual task handled by individual engineers is increasingly performed by autonomous systems that learn from data, adjust in real time, and scale effortlessly across hundreds or thousands of wells. Recent case studies illustrate how this transformation is unfolding across different lift types, each demonstrating that the age of autonomy in artificial lift is no longer aspirational but is now an operational reality. Neural-network models trained on ESP data sets in the Permian Basin now recommend and directly write optimal pump set points, as shown in paper SPE 219528. The deployment delivered a 2–4% oil uplift and longer run life, while demonstrating full self-pumping capability, where pumps operate with minimal human oversight. In gas lift, ExxonMobil’s large-scale rollout of a closed-loop optimization workflow has been equally impactful. As detailed in paper SPE 219553, more than 1,300 wells are now managed by an automated system that runs multirate tests, updates downhole pressure models, and applies ML for wells without gauges. The outcome: a consistent approximately 2% production uplift, achieved with little incremental operational or capital expenditure. On the beam-pump side, manual interpretation of dynamometer cards is being replaced by real-time ML classification. In paper SPE 224979, more than 700,000 dynacards were analyzed with models such as XGBoost and convolutional neural networks, achieving approximately 95% classification accuracy. By integrating with OSIsoft AF and exception-based surveillance, the solution provides predictive analytics and proactive alerts, reducing downtime and extending equipment life. When comparing the three approaches, some clear distinctions emerge. In ESP optimization (SPE 219528), operators relied on neural networks to recommend and autonomously adjust set points, achieving production uplift across more than 200 wells. Gas lift optimization (SPE 219553) took a broader fieldwide approach, scaling a closed-loop workflow to more than 1,300 wells, where multirate testing and ML models continuously updated injection rates, delivering uplift at minimal incremental cost. Meanwhile, beam-pump optimization (SPE 224979) focused less on direct production uplift and more on reliability and predictive surveillance, where ML models enabled proactive maintenance and reduced downtime. Despite their differences, the three initiatives share commonalities: Each system moved from manual, engineer-driven adjustments to automated and autonomous optimization; each leveraged ML in combination with existing field infrastructure; and each demonstrated that even modest per-well improvements, when scaled across hundreds or thousands of wells, translate into substantial fieldwide gains. Across ESPs, gas lift, and beam pumps, these case studies demonstrate a unifying shift: ML is redefining artificial lift as a self-optimizing, continuously learning system. Production uplifts of 2–4%, when multiplied across fields, translate into substantial incremental barrels. Equipment longevity improves as systems learn to operate within optimal ranges, while engineers are freed from repetitive monitoring to focus on higher-value analysis. The advances described here are more than isolated technical improvements; they are signposts of a broader digital transformation across upstream operations. Artificial lift, long the domain of manual adjustments and engineer intuition, is now becoming a proving ground for autonomy. By embedding AI into the very core of production systems, operators are not just optimizing individual wells; they are building the foundation of the digital oilfield of the future, where decisions are data-driven, workflows are autonomous, and engineers focus their expertise on complex challenges rather than routine tuning. Summarized Papers in This October 2025 Issue SPE 219528 - Machine Learning Optimizes Autonomous ESP Use in the Permian by Ryan D. Erickson, Vital Energy, et al. SPE 219553 - Gas Lift Optimization in the Permian Uses Machine Learning, Artificial Intelligence by Pooya Movahed, ExxonMobil, et al. SPE 224979 - Real-Time Machine Learning Enhances Dynacard Surveillance, Predictive Analytics by Shifa Khaliah Al Kiyumi, Daleel Petroleum, et al.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

AI has rapidly changed how web applications are created and delivered in recent years. Using AI brings the user benefits of better automated processes through intelligent self-service, personalization of user experiences/customers, predictive analysis, and now includes real-time decision making. Before AI, web applications were mostly static or rules-based with limited or no change in response to how users interact with them. The introduction of advanced learning technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) has enabled web applications to become "intelligent" platforms that can receive input from a large volume of data and make adjustments in performance to continuously improve Keywords:Artificial Intelligence (AI), Web Applications, Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Recommendation Systems, Chatbots, Predictive Analytics, Data Mining.

The banking industry is undergoing a significant transformation driven by the integration of data-driven approaches aimed at enhancing customer experience. This evolution is essential for banks to maintain competitive advantage, foster customer loyalty, and adapt to the rapidly changing digital landscape. By leveraging vast amounts of customer data, banks can gain profound insights into customer behavior, preferences, and needs, enabling the delivery of more personalized, efficient, and secure services. This review delves into the key techniques employed in data-driven banking and the consequential outcomes. Advanced data analytics allows banks to segment their customer base into distinct groups based on demographics, behaviours, and financial needs. Techniques such as machine learning algorithms and clustering identify patterns within the data, facilitating the creation of targeted products and services. For instance, digital banking solutions can be specifically designed for tech-savvy millennials, while tailored financial advice might be more suitable for older customers. This segmentation helps in addressing the unique needs of different customer groups more effectively. Utilizing historical data, predictive models can forecast future customer behaviors and trends. This capability enables banks to anticipate customer needs, such as predicting which customers might be interested in applying for a mortgage or those who could benefit from financial advisory services. Predictive analytics also aids in identifying customers at risk of churn, allowing banks to implement retention strategies proactively. Through data-driven insights, banks can offer highly personalized experiences. Recommendation systems, akin to those used by leading e-commerce platforms, suggest relevant financial products and services based on individual customer profiles and transaction histories. This level of personalization not only enhances the customer experience but also increases the likelihood of successful cross-selling and upselling efforts. Employing Natural Language Processing (NLP) and sentiment analysis, banks can analyze customer feedback from various sources, including social media, surveys, and call center interactions. Understanding customer sentiment helps banks promptly address issues, improve customer service quality, and build stronger, positive relationships with their customers. Data-driven approaches are pivotal in identifying and mitigating fraudulent activities. Machine learning models analyze transaction patterns to detect anomalies indicative of fraud. Real-time monitoring systems can flag suspicious activities, thereby protecting customers and maintaining their trust in the banking system. Personalized and proactive service offerings significantly boost customer satisfaction. Customers appreciate tailored experiences and quick resolution of issues, leading to higher levels of satisfaction and increased brand loyalty. Automation of routine tasks and predictive maintenance of customer relationships streamline banking operations. This efficiency reduces the workload on bank staff, allowing them to focus on more complex customer needs, resulting in cost savings and improved service delivery. Predictive analytics and real-time monitoring strengthen risk management capabilities. Banks can better assess credit risks, manage fraud, and ensure compliance with regulatory requirements, thus safeguarding their assets and reputation. Effective targeting and personalization drive increased product uptake and customer retention. Enhanced cross-selling and upselling opportunities contribute to revenue growth and profitability, as banks can better match products to customer needs. Insights derived from customer data inform the development of new products and services tailored to evolving customer needs. This continuous innovation keeps banks competitive in a rapidly changing financial landscape. The integration of data-driven approaches in banking significantly enhances customer experience by providing personalized, efficient, and secure services. The application of advanced data analytics, machine learning, and NLP not only boosts customer satisfaction and loyalty but also drives operational efficiency, improves risk management, and fosters revenue growth. As banks continue to innovate with data-driven strategies, the future of customer experience in banking appears promising and dynamic. The ability to adapt and evolve with technological advancements will be crucial in maintaining a customer-centric focus and staying ahead in the competitive financial sector. Keywords: Data-Driven, Banking, Customer Feedback.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

The digital interactions have undergone an exponential trend that has fundamentally shifted the customer relationship management (CRM) systems, and advanced technological interventions are required to handle the more intricate expectations and behavior of the customer. The conventional CRM strategies are unable to handle large volumes of unstructured customer information, forecast subtle behavioral patterns, and provide personalized experiences at a large scale to generate lower customer satisfaction and loyalty. This is a broad literature review on the impacts of machine learning (ML) and deep learning (DL) in revolutionizing CRM practices in increasing customer experience, generating sustainable value, engaging, and gaining long-term loyalty. This paper is a systematic review conducted by relying on the PRISMA methodology to gather and analyze the current research. As it has been shown in the review, ML and DL technologies such as natural language processing, recommendation systems, predictive analytics, and sentiment analysis began to have impressive advantages in terms of the accuracy of customer segmentation, the ability to predict churn, lifetime value estimation, and personalization strategies. More so, incorporation of generative AI, federated learning, and explainable AI models is the next innovation in the development of transparent, privacy and ethically sound CRM systems. The study reveals that there are important gaps in the existing literature about the concepts of real-time emotion recognition, cross-channel behavior synthesis and sustainable AI implementations, and proposes the opportunities in edge computing integration, quantum machine learning applications, and neuromorphic computing to optimize CRM.

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.

Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare includePredictive Analytics,Medical Image Analysis,Drug Discovery,Personalized Medicine,and Electronic Health Records (EHR) Analysis.It has become one of the advanced and popular tool forcomputer sciencedomain.' Theadvancementof ML and DL forvariousfieldshas opened new avenues for research and development. It could revolutionize prediction and decision-makingcapabilities. Due to increased awarenessabout the ML and DL in the healthcare, it has become one of the vital approaches for thesector. High-volumeof unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble forhealthcare sector. The current study uses analysis to examine research trends inadoption of machine learning and deep learningapproaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCIjournals areused as the datasetsfor the comprehensive analysis. Apart from these various search strategy are utilised for therequisitescientificanalysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and authorbased analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the currentstudy will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.

<span lang="EN-US">The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS.</span>

Today’s security attacks on computer networks are becoming more complex and severe, which has prompted security researchers to use a variety of machine learning techniques to safeguard the information and reputation of their clients. Detecting network infiltration has long been a difficult task. Machine learning advancements have raised the way for improving intrusion detection systems (IDS). This development has led to intrusion detection’s integration into network security. Using supervised machine learning techniques, intrusion detection has attained great detection accuracy. However, it is unlikely that a machine learning (ML) classifier will be able to correctly identify all attacks, particularly obscure ones.An approach based on deep learning is presented for more precise intrusion detection. This review article presents an extensive survey and classification of deep learning-based intrusion detection techniques with an emphasis on these approaches. The main background ideas about the IDS architecture and several machine and deep learning approaches are initially presented. Then, it categorizes these schemes based on the many types of methodologies each one employs. It explains how accurate intrusion detection is achieved through the use of machine and deep learning networks. The researched IDS frameworks are then fully analysed, with final thoughts and suggested directions for the future underlined.

The integration of machine learning (ML) models with cloud computing has transformed the landscape of predictive analytics, offering scalable, efficient, and flexible solutions for organizations. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure enable businesses to deploy and manage complex ML models without the need for extensive on-premise infrastructure. However, optimizing these ML models for performance and cost-efficiency in cloud environments presents unique challenges, including resource management, latency, scalability, and data security. This paper focuses on strategies to optimize machine learning models specifically for predictive analytics in cloud environments. It explores key techniques such as auto-scaling, model compression, and hyperparameter tuning, which are critical for improving the accuracy and speed of predictions while minimizing computational costs. The research also examines advanced tools such as containerization, serverless computing, and cloud-native services that further streamline the deployment and management of ML models. In the Indian context, where cloud adoption is growing rapidly, optimizing ML models is crucial for businesses across various sectors, including finance, healthcare, and e-commerce. By leveraging cloud-based ML solutions, Indian companies can enhance their predictive analytics capabilities, driving smarter decision-making and operational efficiency. This abstract presents an overview of how optimized machine learning models can unlock the full potential of predictive analytics in cloud environments, leading to better business outcomes. Through case studies and practical applications, this paper provides actionable insights into the best practices for optimizing ML models in a cloud-based setting.

Machine learning (ML) and artificial intelligence (AI) systems depend heavily on high-quality, structured data to function effectively. Data engineering plays a fundamental role in creating, managing, and optimizing the pipelines and infrastructure that allow for the seamless flow of data across various stages of the ML/AI workflow. This includes the critical tasks of data ingestion, cleaning, preprocessing, and feature engineering, which ensure that the raw data is transformed into a form suitable for model training. Without solid data engineering practices, AI/ML models are prone to inaccuracies, scalability issues, and poor performance. A robust data engineering foundation is vital for enabling the development of efficient models that can scale and produce reliable results over time. This article investigates the essential role of data engineering in ensuring the success of ML/AI systems. It highlights key data engineering practices such as automation of data pipelines, efficient data storage, and management techniques that ensure data quality and reliability. The study also delves into the impact of modern architecture, including cloud-based solutions and automation tools, which help in reducing human intervention and improving the speed of model deployment. Through the examination of real-world case studies, the paper demonstrates how well-executed data engineering practices lead to faster, more accurate model development, lower operational costs, and improved scalability. Ultimately, the article emphasizes that data engineering is indispensable for organizations aiming to unlock the full potential of their AI and ML initiatives, driving innovation and competitive advantage.

This paper shows a method for building and publishing datasets in commercial services. Datasets contribute to the development of research in machine learning and recommender systems. In particular, because recommender systems play a central role in many commercial services, publishing datasets from the services are in great demand from the recommender system community. However, the publication of datasets by commercial services may have some business risks to those companies. To publish a dataset, this must be approved by a business manager of the service. Because many business managers are not specialists in machine learning or recommender systems, the researchers are responsible for explaining to them the risks and benefits. We first summarize three challenges in building datasets from commercial services: (1) anonymize the business metrics, (2) maintain fairness, and (3) reduce the popularity bias. Then, we formulate the problem of building and publishing datasets as an optimization problem that seeks the sampling weight of users, where the challenges are encoded as appropriate loss functions. We applied our method to build datasets from the raw data of our real-world mobile news delivery service. The raw data has more than 1,000,000 users with 100,000,000 interactions. Each dataset was built in less than 10 minutes. We discussed the properties of our method by checking the statistics of the datasets and the performances of typical recommender system algorithms.