Analyzing the Use of Artificial Intelligence and MachineLearning in Customer Support Systems

The purpose of this research is to investigate the nature of the relationship between artificial intelligence (AI) and machine learning (ML) technologies in service-based organizations on the one hand and organizational resilience on the other, as well as the potential impact of external market shocks on this relationship. A quantitative survey of 171 service-based groups was used to perform the research. The study's findings indicate that using AI and ML technologies in service delivery has a beneficial and substantial effect on organizational resilience. Furthermore, the research discovered that the degree to which external market shocks influence the efficacy of these technologies in improving an organization's resilience moderates the connection between the use of AI and ML technologies and organizational resilience. According to the results, service-based companies can improve their resilience by incorporating AI and ML technologies into their service delivery processes. They must, however, evaluate the possible influence of external market shocks on the efficacy of these technologies in increasing resilience. The research has ramifications for service-based organizations and adds to the body of knowledge on organizational resilience and the application of AI and ML technologies in service delivery.

In the food delivery sector, the usage of artificial intelligence (AI) technologies has grown significantly. An important player in the Indian market for online food ordering and delivery, Swiggy, has been looking into how AI could improve its business processes and customer support. This topic intends to analyze the research on artificial intelligence (AI) utilisation in the food delivery sector and investigate its possible uses for Swiggy. The paper covers several AI applications and their effects on many business elements, such as logistics optimization algorithms, NLP-based chat bots, and picture recognition-based recommendation systems. According to the study, Swiggy could boost customer service, increase delivery efficiency, and give users tailored recommendations if it used AI. However, it is essential to assure the moral use of AI and address any potential privacy and prejudice issues. The study comes to the conclusion that AI offers a lot of potential for enhancing Swiggy's business processes and customer service, which would eventually improve customer satisfaction and retention. To do the findings of this topic the sample size of 400 restaurants were taken into account to know the customer preference and the service provided by the restauramt partners in the Swiggy platform and to rectify there problems and issues faced by them in accepting the orders and their online presence in the Swiggy app so that there customer ratings and service can be increased.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

Machine Learning (ML) algorithms, as the core technology in Artificial Intelligence (AI) applications, such as self-driving vehicles, make important decisions by performing a variety of data classification or prediction tasks. Attacks on data or algorithms in AI applications can lead to misclassification or misprediction, which can fail the applications. For each dataset separately, the parameters of ML algorithms should be tuned to reach a desirable classification or prediction accuracy. Typically, ML experts tune the parameters empirically, which can be time consuming and does not guarantee the optimal result. To this end, some research suggests an analytical approach to tune the ML parameters for maximum accuracy. However, none of the works consider the ML performance under attack in their tuning process. This paper proposes an analytical framework for tuning the ML parameters to be secure against attacks, while keeping its accuracy high. The framework finds the optimal set of parameters by defining a novel objective function, which takes into account the test results of both ML accuracy and its security against attacks. For validating the framework, an AI application is implemented to recognize whether a subject's eyes are open or closed, by applying k-Nearest Neighbors (kNN) algorithm on her Electroencephalogram (EEG) signals. In this application, the number of neighbors (k) and the distance metric type, as the two main parameters of kNN, are chosen for tuning. The input data perturbation attack, as one of the most common attacks on ML algorithms, is used for testing the security of the application. Exhaustive search approach is used to solve the optimization problem. The experiment results show k = 43 and cosine distance metric is the optimal configuration of kNN for the EEG dataset, which leads to 83.75% classification accuracy and reduces the attack success rate to 5.21%.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

The rate of development in artificial intelligence (AI) technologies has brought revolutionary transformationsacross the healthcare sector. The advancements especially impacted the manner in which the diagnosis,treatment, and management of diseases are undertaken with a growing emphasis laid on optimizing clinicalworkflow and patient care. Machine learning (ML) and deep learning (DL) technologies have proved invaluablefor the analysis of complex datasets, supporting early diagnosis and optimizing treatment protocols. AIshowcased tremendous promise in a plethora of applications, such as the accurate analysis of medical imagery,the personalization of therapies through the tailor-made dosing of medicines, predictive analysis in forecastingthe course of the disease, and the assistance of patients and clinicians by deploying virtual health tools.Moreover, the increasing application of AI in healthcare has also boosted access to quality healthcare,particularly in underserved populations. The following review also explores the ethical, legislative, andregulatory issues related to the application of AI in medicine, reiterating the need for protection of the data,transparency, and equitable access. With careful implementation that respects these boundaries, AI holds thepotential to significantly enhance global healthcare delivery. Despite these benefits, integrating AI intohealthcare systems raises important ethical, legal, and regulatory concerns. Ensuring data privacy, promotingalgorithm transparency, and maintaining fairness in AI-driven healthcare solutions are key concerns.Addressing these challenges is vital to building trust and ensuring that AI technologies benefit all populationsfairly. With responsible and ethical implementation, AI holds immense potential to transform global healthcaredelivery and improve patient outcomes on a large scale. How to cite this: Jan TA, Naz S. Artificial Intelligence in Medicine: Transforming The Future of Healthcare. Life and Science. 2025; 6(2): 284-291. doi: http://doi.org/10.37185/LnS.1.1.904

The rapid advancement of information technology has led to an upsurge in cybercriminal activities. As technology continues to evolve, so do the tactics used by individuals involved in digital offenses. Trends in complex, distributed, and internet[1]based computing have raised significant concerns regarding information security and privacy. Cyber infrastructures, in particular, are highly susceptible to intrusions and various threats. Traditional security measures like sensors and detectors are inadequate for safeguarding these infrastructures, necessitating the development of more sophisticated IT solutions capable of modeling normal behaviors and identifying anomalies. To address these challenges effectively, cyber defense systems must exhibit traits such as flexibility, adaptability, and robustness. They should be able to detect a wide range of threats while making intelligent real-time decisions. Given the sheer volume and speed of cyberattacks, relying solely on human intervention is inadequate for prompt analysis and response. Many of these attacks are orchestrated by intelligent agents like computer worms and viruses, making it essential to combat them using intelligent, semi-autonomous agents that can promptly detect, evaluate, and respond to cyber threats. These computer-generated forces must manage the entire process of responding to attacks, encompassing the identification of the attack type, its targets, the appropriate response, and the prioritization of secondary attack prevention. Furthermore, cyber intrusions are not confined to a single location; they represent a global menace to computer systems worldwide. The expansion of the internet has made knowledge and tools for cybercrime readily accessible to a wide audience, no longer limited to educated specialists. Traditional, rigid algorithms with hard-wired logic have proven ineffective in countering dynamically evolving cyberattacks. This underscores the importance of innovative approaches, particularly the application of Artificial Intelligence (AI), to enhance our capability to combat cybercrimes. AI introduces flexibility and learning capabilities to software, thereby assisting humans in the fight against cybercrimes. Various AI techniques, inspired by nature, including Computational Intelligence, Neural Networks, Intelligent Agents, Artificial Immune Systems, Machine Learning, Data Mining, Pattern Recognition, Fuzzy Logic, and Heuristics, are playing an increasingly crucial role in the detection and prevention of cybercrimes. AI empowers the design of autonomic computing solutions that can adapt to their usage context, employing methods such as self-management, self-tuning, self[1]configuration, self-diagnosis, and self[1]healing. In the realm of information security, AI represents a promising area of research with a focus on enhancing cybersecurity measures in cyberspace. The term "Artificial Intelligence" is used to describe a machine's ability to emulate human-like activities, including problem solving and learning, a concept often referred to as machine learning. The next generation of cybersecurity products is increasingly incorporating Artificial Intelligence and Machine Learning technologies. By analyzing extensive datasets of cybersecurity, network, and physical information, providers of cybersecurity solutions aim to identify and thwart abnormal behavior. Various approaches are employed to utilize AI for cybersecurity. Some applications analyze raw network data to detect irregularities, while others focus on user-entity behavior to identify deviations from the norm. The choice of approach depends on the type of data streams and the level of effort required by analysts

Machine Tools’ Running State Monitoring in the Learning Factory—Based on Machine Learning Method and Vibration Data

Recent advances and applications of artificial intelligence and related technologies in the food industry

Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.

Industry 4.0 brought with it by the next-gen Industry 5.0 and Society 5.0 paradigms, catalysed by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies. These advances have the benefit of encouraging sustainability, improving output, and updating manufacturing. By enabling self-decision, continuous monitoring, and predictive maintenance with the processing of large data, AI is dramatically reducing downtime and associated costs of system downtime. As a result, ML algorithms, in light of their applicability for continuous learning and adaptation, have contributed to enriching product quality, streamlining supply networks and okaying personalized customer experiences. Neural networks are also being leveraged to improve computer vision and speech capabilities, for applications such as smart automation and human-robot cooperation in challenging industrial contexts. Industry 5.0 truly puts humans back at the centre of innovation. It is aimed to create an evolved society in which AI, ML, and DL are fused with the digital and physical world Society 5.0. The integration aims to address a plethora of societal challenges: environmental sustainability, health and ageing population, among others. It is a convergence of these said technologies that lead to a paradigm shift towards more resilient, adaptive, and sustainable industrial ecosystems. This paper aims to address these questions in a systematic way to offer a comprehensive view of what the future industrial landscape could look like leveraging the promise of Industry 4.0 and Industry 5.0 thus, and more opportunities to embrace intelligent and sustainable industries of tomorrow.

The energy industry worldwide is today confronted with several challenges, including heightened levels of consumption and inefficiency, volatile patterns in demand and supply, and a dearth of crucial data necessary for effective management. Developing countries face significant challenges due to the widespread occurrence of unauthorized connections to the electricity grid, resulting in substantial amounts of unmeasured and unpaid energy consumption. Nevertheless, the implementation of artificial intelligence (AI) and machine learning (ML) technologies has the potential to improve energy management, efficiency, and sustainability. Therefore, this study aims to evaluate the potential influence of AI and ML technologies on the progress of the energy industry. The present study employed the systematic literature review methodology to examine the challenges arising from frequent power outages and limited energy accessibility in various developing nations. The results of this study indicate that AI and ML possess significant potential in various domains, including predictive maintenance of turbines, optimization of energy consumption, management of power grids, prediction of energy prices, and assessment of energy demand and efficiency in residential buildings. This study concluded with a discussion of the necessary measures to enable developing nations to harness the advantages of AI and ML in the energy sector.

Advent of Artificial Intelligence (AI) and Machine Learning (ML) technology has ushered people into an era that was once considered a part of science fantasy novels. The level of current AI application is not as advanced as to give rise to a robot uprising and put human existence in danger. The immediate serious concerns are related to issues like power imbalance and societal divide that AI can create, labour issues that will be raised, privacy of users and serious threats to democracy due to disinformation capabilities powered by AI tools. The entire process of conceptualization, design, training and deployment of sophisticated AI solutions is often very costly. Access to human resource in developing AI solutions is often difficult for smaller companies, non-profit organizations and developing/underdeveloped countries. Poorly designed AI systems can further shift the power balance of the society by unintentionally or intentionally discriminating against certain members of the society.