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Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment
In artificial intelligence (AI), machine learning (ML) has become a game-changing concept that allows systems to learn from experience and get better without explicit programming. This chapter explores the main ideas, techniques, and applications of ML, offering a succinct introduction to the field. The first step in the process is to gain a basic understanding of supervised learning, which is the process by which algorithms learn to make predictions or judgements from labelled training data. Next, we introduce unsupervised learning, which emphasizes finding patterns in unlabelled data and frequently results in interesting findings and clustering. To emphasize the importance of reinforcement learning in decision-making processes, the paradigm is presented where agents learn by interacting with an environment and receiving feedback. Ideas related to ML, such as feature engineering, model assessment, and the balance between variance and bias, are discussed. The significance of quality data in ML applications is emphasized, along with the impact of data pretreatment on model performance. It also clarifies how neural networks, a branch of ML, simulate the workings of the human brain. The ability of deep learning, a branch of ML that makes use of multi-layered neural networks, to handle challenging tasks such as speech and picture recognition is being investigated. In order to emphasize the necessity of responsible ML model deployment and usage, practical factors are emphasized, including the significance of ethical considerations and responsible AI. The final section of the chapter offers a preview of MLs future, discussing issues and trends that practitioners and researchers should be aware of. This chapter essentially functions as a thorough introduction to ML principles, providing an overview of the wide range of ML approaches, applications, and ethical issues that support the technologys transformative potential across a range of industries. 2025 selection and editorial matter, G. Sucharitha, Anjanna Matta, M. Srinivas and Sachi Nandan Mohanty; individual chapters, the contributors. -
Leveraging machine learning models for intelligent hazard management
[No abstract available] -
A Reliable Method of Predicting Water Quality Using Supervised Machine Learning Model
Water contributes to around 70% of the world's exterior and is perhaps the primary source essential to supporting life. The rapid growth of urban and industrial geographies has prompted a disintegration of the quality of water at a concerning pace, bringing about nerve-racking sicknesses. Water quality has been expectedly assessed through costly and tedious lab and measurable examinations, which render the contemporary thought of continuous observing disputable. The disturbing results of helpless water quality require an elective strategy, which is speedier and more economical. With this inspiration, this exploration investigates a progression of administered AI calculations to appraise the Water Quality Index (WQI), which acts as a unique attribute to express the generic nature of water. The proposed system utilizes multiple info boundaries, specifically, temperature, pH, dissolved O2 concentration, and all-out broken down molecules. Of the multitude of utilized regression calculations and slope boosting, the water quality index can be expected most productively, with an MSE of 0.27. The propositioned study accomplishes acceptable precision by utilizing a minimum number of features to improve the chances of it getting implemented progressively in water quality recognition frameworks. 2022 IEEE. -
Edge intelligence to smart management and control of epidemic
The effects of COVID-19 vary from person to person. A pandemic is devastating economically and socially. Thousands of enterprises face the possibility of collapse. More than half of the world's 3.3 billion workers may lose their livelihoods if the current crisis continues. The world's healthcare services are facing an unprecedented situation due to the recent outbreak of a novel coronavirus (COVID-19). Community and government health are adversely affected by the COVID-19 pandemic. COVID-19 has continued to spread, and mortalities have risen steadily. The spread of this disease can therefore be controlled utilizing nonpharmacological methods, such as quarantine, isolation, and public health education. Recent breakthroughs in deep learning (DL) have led to an explosion in applications and services relating to artificial intelligence (AI). The rapid advancements in mobile computing and AI have enabled zillions of Bytes of data to be generated at the network edge from thousands of mobile devices and internet of things (IoT) devices connected to the Internet. As a result of the success of IoT and AI technologies, it is of utmost importance that we expand the AI frontiers to the network edge in order for big data to be fully tapped. Edge computing (EC) can help overcome this trend because it allows computation-intensive AI applications to run on edge hardware. The topic of discussion in this chapter is edge intelligence (EI) technology's application in limiting virus spread during pandemics. 2024 Apple Academic Press, Inc. All rights reserved. -
Quantum Information Processing for Legal Applications through Bloch Sphere of Law
The objective of the research work is to propose a quantum information processing model (QIP) for legal applications including litigation and investigation phases. The quantum information processing and quantum computing concepts can be visualized within a Bloch Sphere of Law (BSL) as legal Bloch vectors (LBV) as quantum computing entities. This quantum approach is needed since the complexity of legalities and the legal objects involved in the final judgement are to be reversible with a lot of uncertainties. The reasoning and prosecution through various trials and investigations are to be considered as mathematical matrix or unitary operations in this muti dimensional legal space. The mapping of legal information into technical and then vectorial representations are deployed through a glossary of legal terms in this quantum paradigm. As a forerunning study and application in the quantum paradigm, mathematical and computational models have been proposed in the work with a case study of a recent civil case. 2022 IEEE. -
Optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 20232024
This research paper aims to analyze and optimize portfolios for the top funded industries based on the budget23. The study uses a data-driven approach to identify the best investment opportunities within these industries. The methodology involves collecting financial data, conducting market analysis, and using optimization techniques to create an optimal portfolio. The results of the study show that the top funded industries have a high potential for growth, and the optimized portfolios can maximize returns while minimizing risk. The findings can provide valuable insights for investors and fund managers who are seeking to make informed investment decisions in these industries. The study also highlights the importance of considering the budget constraints while optimizing portfolios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
IoT and Sustainability Energy Systems: Risk and Opportunity
As IoT (Internet of Things) and smart technologies have developed rapidly, many technological advancements have been made possible. The IoTs main objective is to assist in simplifying processes in a number of different felds, to improve the effciency of technologies and protocols, and ultimately to improve quality of life. Although IoT technologies can beneft the population in numerous ways, their development must be evaluated from an environmental viewpoint to ensure that global resources are used effciently and to prevent negative effects. As previously described, considerable research effort is needed to explore the advantages and disadvantages of IoT technologies. Engineering professionals, industrial experts, and academic researchers successfully interacted at the conference. Several key tracks made up the conference, including smart city, energy and environment, e-health, and engineering modeling. Specifcally, the editorial covered a number of topics including (i) IoT in sustainable energy and environmental management, (ii) smart cities enabled by IoT, (iii) ambient assisted living, and (iv) IoT technologies for transportation and low-carbon products. An important outcome of our introductory analysis has been a greater understanding of both the scientifc developments in IoT applications and the potential ecological consequences associated with increasing IoT applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
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Narratives of the self: Comments and confessions on Facebook
Narratives are structured around events, which are used to tell a story. The self is perpetually being constructed through narratives of experience. This chapter focuses on the phenomenon of Facebook confession pages and how they contribute to the construction of digital identity. Drawing on insights from my project on the role of Facebook College Confession pages, the chapter examines how these platforms have transformed the way users express and shape their identities. The anonymity provided by these pages allows users to post confessions without revealing their identities, encouraging a form of virtual self-exploration. These confessions, often written by nameless authors, generate a complex and ongoing narrative of identity, shaped by the interaction of multiple voices and viewpoints. The chapter also explores the motivations behind sharing personal confessions, even when the responses may be negative, and how this contributes to the perpetual construction of the digital self. By examining the intersection of public and private spheres in these online spaces, this chapter highlights how the breaking of the public-private divide enables users to create and negotiate their identities in a digital, networked world. The narrative constructed is endless, and the post is not an end in itself. It paves the way for the generation of an endless narrative by multiple authors with multiple viewpoints. This chapter explores the reasons behind sharing such posts on Facebook, even if the comments are negative in tone. It will refer to Anthony Giddens' concept of time-space "distanciation" (Keefer et al., 2019) to show how multiple tellers through their narratives help to build the complex networked identity of a user. The study will also analyse the role played by the breaking of the public-private divide in creating such spaces for the construction of a private self through public voices. 2024 Rimi Nandy. -
Finding balance in a digital world: Equanimity as a predictor of nomophobia
The present study examined the relationship between equanimity and nomophobia. The study also examined the differences in experience of nomophobia considering gender, education and employment status. The sample included 216 emerging adults (M = 64, F = 152) from across India. The Equanimity Scale 16 and the Nomophobia Questionnaire were used to measure equanimity and nomophobia, respectively. Mann-Whitney-U test and Rank-Biserial coefficient indicated that gender differences significantly affected the losing connectedness factor of nomophobia. Correlation analysis showed that equanimity had a significant negative relationship with nomophobia and its factors- not being able to access information, giving up convenience and losing connectedness. Regression analysis showed equanimity as a significant predictor of nomophobia. The studys findings hold potential implications for equanimity-based interventions for nomophobia and individual well-being, technological design improvements in the digital age and unfolds areas for future research. 2024 Taylor & Francis Group, LLC. -
To study the factors of consumer involvement in fashion clothing /
International Journal Of Science And Research, Vol.3, Issue 7, pp.542-546, ISSN No: 2319-7064. -
Updated aspects of alpha-Solanine as a potential anticancer agent: Mechanistic insights and future directions
Cancer remains a critical global health challenge, with limited progress in reducing mortality despite advancements in diagnosis and treatment. The growing resistance of tumors to existing chemotherapy exacerbates this burden. In response, the search for new anticancer compounds from plants has intensified, given their historical success in yielding effective treatments. This review focuses on ?-solanine, a glycoalkaloid primarily derived from potato tubers and nightshade family plants, recognized for its diverse biological activities, including anti-allergic, antipyretic, anti-inflammatory, anti-diabetic, and antibiotic properties. Recently, ?-solanine has gained attention as a potential anticancer agent. Utilizing resources like PubMed/MedLine, ScienceDirect, Web of Science, Scopus, the American Chemical Society, Google Scholar, Springer Link, Wiley, and various commercial websites, this review consolidates two decades of research on ?-solanine's anticancer effects and mechanisms against nine different cancers, highlighting its role in modulating various signaling pathways. It also discusses ?-solanine's potential as a lead compound in cancer therapy. The abundant availability of potato peel, often discarded as waste or sold cheaply, is suggested as a sustainable source for large-scale ?-solanine extraction. The study concludes that ?-solanine holds promise as a standalone or adjunctive cancer treatment. However, further research is necessary to optimize this lead compound and mitigate its toxicity through various strategies. 2024 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC. -
Magical mushroom Ganoderma-A Promising treatment for cancer
[No abstract available] -
Anticancer activity and other biomedical properties of ?-sitosterol: Bridging phytochemistry and current pharmacological evidence for future translational approaches
Sterols, including ?-sitosterol, are essential components of cellular membranes in both plant and animal cells. Despite being a major phytosterol in various plant materials, comprehensive scientific knowledge regarding the properties of ?-sitosterol and its potential applications is essential for scholarly pursuits and utilization purposes. ?-sitosterol shares similar chemical characteristics with cholesterol and exhibits several pharmacological activities without major toxicity. This study aims to bridge the gap between phytochemistry and current pharmacological evidence of ?-sitosterol, focusing on its anticancer activity and other biomedical properties. The goal is to provide a comprehensive understanding of ?-sitosterol's potential for future translational approaches. A thorough examination of the literature was conducted to gather relevant information on the biological properties of ?-sitosterol, particularly its anticancer therapeutic potential. Various databases were searched, including PubMed/MedLine, Scopus, Google Scholar, and Web of Science using appropriate keywords. Studies investigating the effects of ?-sitosterol on different types of cancer were analyzed, focusing on mechanisms of action, pharmacological screening, and chemosensitizing properties. Modern pharmacological screening studies have revealed the potential anticancer therapeutic properties of ?-sitosterol against various types of cancer, including leukemia, lung, stomach, breast, colon, ovarian, and prostate cancer. ?-sitosterol has demonstrated chemosensitizing effects on cancer cells, interfering with multiple cell signaling pathways involved in proliferation, cell cycle arrest, apoptosis, survival, metastasis invasion, angiogenesis, and inflammation. Structural derivatives of ?-sitosterol have also shown anti-cancer effects. However, research in the field of drug delivery and the detailed mode of action of ?-sitosterol-mediated anticancer activities remains limited. ?-sitosterol, as a non-toxic compound with significant pharmacological potential, exhibits promising anticancer effects against various cancer types. Despite being relatively less potent than conventional cancer chemotherapeutics, ?-sitosterol holds potential as a safe and effective nutraceutical against cancer. Further comprehensive studies are recommended to explore the biological properties of ?-sitosterol, including its mode of action, and develop novel formulations for its potential use in cancer treatment. This review provides a foundation for future investigations and highlights the need for further research on ?-sitosterol as a potent superfood in combating cancer. 2023 John Wiley & Sons Ltd. -
Dolastatins and their analogues present a compelling landscape of potential natural and synthetic anticancer drug candidates
Human cancer remains a leading cause of global mortality. Traditional treatment methods, while effective are often associated with substantial side effects, high technical requirements, and considerable expenses. Recently, anticancer peptides, such as dolastatin-type peptides naturally found in marine mollusc Dolabella auricularia, have gained attention due to their enhanced characteristics and specific targeting of cancer cells with minimal toxicity to normal cells. This review aims to provide a comprehensive summary of the anticancer activities of natural dolastatins and synthetic analogues over the past 35 years, focusing on their utilization in advancing cancer treatment strategies. This updated review encompasses a detailed analysis of numerous studies demonstrating the cytotoxic effects of dolastatins and their synthetic analogues on various human tumour cell lines. The analysis includes investigations into their ability to activate apoptosis pathways, inhibit cell cycle progression, and indirectly limit inflammation and angiogenesis in tumours. Both natural dolastatins and synthetic analogues have demonstrated significant anticancer properties through a variety of mechanisms in vitro and in vivo pharmacological studies. Some have even advanced to clinical trials, either alone or in combination with other agents, and have shown promising outcomes. The biological activities of dolastatins and their synthetic analogues offer a promising path in the development of more effective and sustainable anticancer drugs. Their specific action on cancer cells and relative non-toxicity to normal cells highlight their potential as superior cancer therapeutic agents. The current study provides a platform for the most recent preclinical and clinical research on dolastatins and their analogues. Further research into these marine peptides may contribute to the development of sustainable and efficient treatment models for cancer, filling a significant gap in the current cancer therapeutic portfolio. 2023 The Authors -
An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE. -
Improving maternal health by predicting various pregnancy-related abnormalities using machine learning algorithms
Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency. 2023 by IGI Global. All rights reserved. -
Review on the Biogenesis of Platelets in Lungs and Its Alterations in SARS-CoV-2 Infection Patients
Thrombocytes (platelets) are the type of blood cells that are involved in hemostasis, thrombosis, etc. For the conversion of megakaryocytes into thrombocytes, the thrombopoietin (TPO) protein is essential which is encoded by the TPO gene. TPO gene is present in the long arm of chromosome number 3 (3q26). This TPO protein interacts with the c-Mpl receptor, which is present on the outer surface of megakaryocytes. As a result, megakaryocyte breaks into the production of functional thrombocytes. Some of the evidence shows that the megakaryocytes, the precursor of thrombocytes, are seen in the lungs interstitium. This review focuses on the involvement of the lungs in the production of thrombocytes and their mechanism. A lot of findings show that viral diseases, which affect the lungs, cause thrombocytopenia in human beings. One of the notable viral diseases is COVID-19 or severe acute respiratory syndrome caused by SARS-associated coronavirus 2 (SARS-CoV-2). SARS-CoV-2 caused a worldwide alarm in 2019 and a lot of people suffered because of this disease. It mainly targets the lung cells for its replication. To enter the cells, these virus targets the angiotensin-converting enzyme-2 (ACE-2) receptors that are abundantly seen on the surface of the lung cells. Recent reports of COVID-19-affected patients reveal the important fact that these peoples develop thrombocytopenia as a post-COVID condition. This review elaborates on the biogenesis of platelets in the lungs and the alterations of thrombocytes during the COVID-19 infection. 2023, SAGE Publications Ltd. All rights reserved. -
Machine Learning Approaches for Suicidal Ideation Detection on Social Media
Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. 2023 IEEE. -
COVID-19 and the cry of the poor sensitivity and solidarity
[No abstract available]