2024

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Some Observations on Social Media Mining tools for Health Applications
(Springer Science and Business Media Deutschland GmbH, 2024) Ankita; Rakhi Garg
The use of social media has immensely increased globally, which accordingly have generated tremendous amounts of data that have in turn attracted many researchers. Accessing, analyzing and mining this Big Social Data bring about a great deal of challenges. Social Media has ample data for various applications including the health domain that we have seen recently during the outbreak of COVID-19. Various applications and frameworks were designed for real-time monitoring and detection of disease spread, patient’s health status, providing information about active cases, hotspot areas, vaccination centers and also for disseminating government advisories. Social Media Mining provides tools and techniques to mine Social Media for extracting information, study patterns and analyze data. Social Media Mining can be used to bridge the gap between current technologies and healthcare systems. This paper basically reviewed various tools, techniques and algorithms developed for Social Media Mining for Health Applications (SMM4HA) and also discussed the major challenges that emanate using SMM4HA such as the issue of health misinformation and rumors, privacy and security issues, issue of data breaches, Big Social Data and the ethical challenges. We have also discussed some recent works in the healthcare domain such as disease surveillance, drug detection, disease prediction, etc., focusing on issues and challenges in machine learning–Natural Language Processing and use of medical ontologies as Social Media Mining Tools and Techniques that will help researchers and scientists working in this area. More research in the area is needed to be done considering rapid change of voluminous Social Media data which require efficient computation models as well as algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Role of quantum technology and artificial intelligence for nano-enabled microfluidics
(Elsevier, 2024) Surendra K. Yadav; Kolleboyina Jayaramulu
Precision medicine aims to recommend tailored treatments for cancer patients, a process facilitated by the integration of artificial intelligence (AI), machine learning (ML), and nanotechnology. This convergence is driving the collection of patient data and enhancing patient outcomes. By utilizing diagnostic nanomaterials, it has become possible to compile a patient's disease profile, enabling the application of various remedial nanotechnologies to aid in the patient's recovery. However, the considerable heterogeneity within cancers presents a significant challenge in devising logical diagnostic and therapeutic strategies, as well as analyzing their outcomes. To bridge this gap, the integration of AI approaches, including pattern investigation and categorization algorithms, has proved invaluable. Applied AI also assumes a pivotal role in the design of nanomedicine, optimizing material characteristics based on projected interactions with biological fluids, target drugs, the vascular system, the immune system, and cell membranes—all of which collectively influence therapeutic efficacy. The synergy of nanotechnology and AI has the potential to completely transform the landscape of precision cancer medicine. Within this chapter, the core tenets of microfluidics and AI are elucidated, alongside an exploration of the imminent impact of nanotechnology. Noteworthy are the diverse applications of machine learning in analyzing microfluidic data, yielding remarkable outcomes. Proposals have been made to synergize microfluidic platforms with closed-loop data-guided models, integrating multimodal monitoring techniques. Beyond establishing a framework for delving into the fundamental principles of materials science and biomedicine, this approach also furnishes insights into domains such as drug discovery, nanomaterials, in vitro organ modeling, and developmental biology. © 2024 Elsevier Inc. All rights reserved.
Artificial intelligence as a smart approach in clinical microbiology laboratory
(Academic Press Inc., 2024) Akanksha Srivastava
Artificial intelligence (AI) is rapidly growing field with potential impact on global health. AI involves an algorithmic rule which have been used to analyse a set of data with an automated system. AI is expeditiously penetrating the field of biotechnology and microbiology with higher impact in clinical laboratories, disease diagnosis and healthcare. In addition, emergence of AI will be highly beneficial for clinical microbiologist, academicians, and researchers. It provides optimal efficiency and quality, with rapid and cost-effective results for disease diagnosis. In this chapter, we elaborate some of the routine diagnosis test occurred in clinical microbiology laboratories. Further, we explore the AI applications in image analysis of microbial staining, bacterial infection, ova and parasite examination, analysis of antimicrobial resistance with whole genome sequencing (WGS) and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is also enlightened here. © 2024 Elsevier Ltd
Artificial intelligence in plant disease identification: Empowering agriculture
(Academic Press Inc., 2024) Tanya Garg; Padmanabh Dwivedi; Manoj K. Mishra; Naveen Chandra Joshi; Neeraj Shrivastava; Vaibhav Mishra
The agricultural sector faces numerous challenges, such as infectious diseases, pest invasions, improper soil management, inadequate watering, and more. Among these, plant infectious diseases stand out as a leading cause of damage to crops. These diseases, impacting plants, result from various factors like genetics, soil composition, precipitation, moisture levels, humidity, temperature, and wind. In recent times, there has been a significant increase in the prevalence of plant infectious diseases. Pathogens like viruses, bacteria, and fungi consistently pose threats to plants, leading to substantial global crop yield losses during disease outbreaks. Unfortunately, disease identification and diagnosis typically occur at an advanced stage, causing significant agricultural setbacks. Given the impact of plant diseases on the nutritional value of fruits, vegetables, organic products, and cereals, timely identification is crucial during cultivation. Artificial intelligence (AI) has emerged as a pivotal tool in this context, leveraging its meticulous training capabilities for the effective identification of infectious plant diseases. This chapter illustrates how AI plays a vital role in identifying and diagnosing contagious plant diseases. © 2024 Elsevier Ltd
Integrative omics data mining: Challenges and opportunities
(Elsevier, 2024) Swarna Kanchan; Minu Kesheri; Upasna Srivastava; Hiren Karathia; Ratnaprabha Ratna-Raj; Bhaskar Chittoori; Lydia Bogomolnaya; Rajeshwar P. Sinha; James Denvir
Next-generation sequencing-based high-throughput data has opened novel opportunities to analyze and describe biological processes at a higher resolution. Nowadays, multiomics technologies are generating large amounts of heterogeneous genomics, proteomics, and metabolomics datasets. Integrative approaches enable us to study complex biological processes that combine the analysis of multiple omics datasets to highlight the interplay of the involved genes, transcripts, proteins, metabolites, etc., and their functions. Thus, data integration and data mining are imperative to exploring the mysteries of life and complex diseases in life sciences research. In the present scenario, integrating heterogeneous and huge amounts of genomics, proteomics, and metabolomics data poses conceptual and practical challenges, and encourages researchers to develop novel data integration methodologies, tools, and virtualization platforms. This chapter reviews the current efforts and state of the art about data integration and its mining in life sciences research. This chapter describes various tools and methods in detail that adopt an integrative approach to analyze multiomics data and data mining methods to address phenotype prediction, disease subtyping, a novel biomarker, novel pathways discovery, etc. This chapter provides an extensive overview in lucid style illustrating the methodologies, limitations of these tools, multiomics data repositories, and visualization platforms along with enumerating the challenges associated with multiomics data integration and mining making this chapter informative and reader friendly. © 2024 Elsevier Inc. All rights reserved.
Plant stress phenotyping: Current status and future prospects
(Academic Press Inc., 2024) Vishal Dinkar; Sayantan Sarkar; Saurabh Pandey; Suresh H. Antre; Amarjeet Kumar; R. Thribhuvan; Ashutosh Singh; Ashish Kumar Singh; Badal Singh; Md. Afjal Ahmad
Scientists aim to improve crop response under stress conditions and gain better yields in continuously changing environmental conditions. They rely on plant phenotyping to quantify crop response under adverse conditions to achieve this goal and select the most tolerant genotypes. Recent advances in phenotyping platforms allow dissecting of complex traits such as abiotic stress. For example, the phenotyping platform is integrated with artificial intelligence (AI) and remote sensing tools to provide more robust, high throughput data collections in real-time changing environments. This review will give a deep understanding of the requirement of phenomics in crop improvement under stress conditions. We have discussed different phenotyping platforms, suitable traits for phenotyping, and machine learning and AI integration with the high throughput phenotypic platform for collecting a large data set of crops under stress conditions. Overall our review will dissect the phenomics aspects of complex traits, such as biotic and abiotic stress-related traits requiring sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines and better our understanding of plant stress biology. © 2024 Elsevier Inc.
Leveraging artificial intelligence (AI) and machine learning (ML) for enhanced drug discovery and development from microbes
(Academic Press Inc., 2024) Vaibhav Mishra; Sandeep K. Mishra; Akanksha Srivastava; Chetan Kumar Dubey; Komal Dharmani; Navaneet Chaturvedi
Artificial intelligence (AI) can be a great helping hand in drug discovery research. Further AI concept is founded upon the idea that human thought and reasoning processes can be properly described, captured, and formally embedded into machines. Moreover, it's a branch of computer science and technology that deals with the creation and development of intelligent machines that can simulate human thinking and perform tasks that typically require human intelligence. Although, AI systems are designed to analyse data, learn from it, and make decisions or predictions based on the patterns they discover. Currently healthcare system is greatly influenced by the big data which is used for the development of drugs and prediction of microbial infectious disease. Additionally, the amalgamation of AI and microbiology exhibits great prospects in research and development, diagnose microbial infectious disease and treat patients remotely, improving communication between patient and clinicians, analysing the medical records, help clinicians to prescribe personalized medicines and develop precise drugs from microbial sources. Overall the field of microbiology has been significantly impacted by the emergence of advanced AI technologies. In this chapter, we discuss the applications of the AI and its advanced tool machine learning (ML) that has worked at an intersection with big data to provide reasonable solution to the health care system. We also, explore the fascinating application of AI and ML technologies in drug discovery from microbes that have been put used in current scenario. © 2024 Elsevier Ltd
Implementation of artificial intelligence (AI) and machine learning (ML) in microbiology
(Academic Press Inc., 2024) Prashant Tripathi; Akanksha Srivastava; Chetan Kumar Dubey; Vaibhav Mishra; Shipra Dwivedi; Amit Kumar Madeshiya
Artificial intelligence (AI) is the ability of a machine to perform a cognitive function that resembles the human brain. In today's world, AI is being largely applied in the field of medical science including clinical microbiology and environmental microbiology. According to a recent report by PwC Middle East, 2% of the total global benefit of AI which is about US$320 billion has been projected by 2030.With the help of AI in the medical field, the way of undertaking emergencies and life-threatening conditions has changed. Its applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. In this chapter, we discuss, what is AI and machine learning and how it is being implemented in the field of clinical microbiology, and environmental microbiology as well as where we need improvement. © 2024 Elsevier Ltd
Unravelling the gut microbiome: Connecting with AI for deeper insights
(Academic Press Inc., 2024) Vaibhav Mishra; Chhavi Atri; Raj Pandey; Akanksha Srivastava
Artificial intelligence (AI) remains a relatively unfamiliar concept for many, but its significance in the biomedical field is gaining recognition as the world undergoes transformative changes. Furthermore, AI possesses the potential to emulate critical thinking, reasoning, problem-solving abilities, and logical capacities of machines. Additionally, in the realm of gut microbiota research, AI emerges as a valuable asset. The synergy between gut microbes and AI not only holds promise for treating diverse gastroenterological diseases but also aids in comprehending the intricate relationships between gut microbes and microbes of resides into the other body parts. Moreover, AI facilitates a deeper understanding of different facets within gut-microbes interaction research. These direct communications are governed by chemical messengers, hormones, and neurotransmitters, detectable through biosensor chips employing machine learning (ML). Additionally, the indirect regulation of gut function by the brain via the hypothalamic-pituitary-adrenal (HPA) axis can be analysed using different computational models. This promising prospect remains largely unexplored, and in this chapter, our aim is to delve into and harness the potential of AI in gut microbial research. © 2024 Elsevier Ltd
Machine Learning-Based Analysis of Code Smells at Class Level and Method Level
(Springer Science and Business Media Deutschland GmbH, 2024) Manjari Gupta; Sripriya Roy Chowdhuri; Abhilasha Ojha
In modern software development, maintaining high-quality code is essential for ensuring system robustness, maintainability, and long-term viability. Code smells, which are indicative of below standard alternatives of software design or potential vulnerabilities, can negatively impact software reliability and developer productivity. This paper confers a comprehensive analysis of code smells in both the aspect of class and methods. So, this study focuses on creating a dataset of code smells at different levels of object-oriented paradigm such as at method level and class level in fourteen Java applications and applies several algorithms based on machine learning, specifically J48, JRip, random forest, and Naive Bayes to examine them. The results show that there are several significant code smells which needs to be explored for better software development. The outcomes in this study are quite promising and will pave the way for researchers working in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

2024

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