Introduction: The advances in the digital era have necessitated the adoption of communication as the main channel for modern business. In the past, business negotiations, profiling, seminars, shopping, and agreements were in-person but today everything is almost digitalized. Objectives: The study aims to examine how the Internet of things (IoTs) connects text-object as part of NLP and AI responding to human needs. Also, how precipitated changes in the business environment and modern applications such as NLP and AI embedded with IoTs services have changed business settings. Problem statement: As communication takes lead in the business environment, companies have developed sophisticated applications of NLP that take human desires and fulfill them instantly with the help of text, phone calls, smart records, and chatbots. The ease of communication and interaction has shown a greater influence on customer choice, desires, and needs. Modern service providers now use email, text, phone calls, smart records, and virtual assistants as first contact points for almost all of their dealings, customer inquiries, and most preferred trading channels. Method: The study uses text content as part of NLP and AI to demonstrate how companies capture customers’ insight and how they use IoTs to influence customers’ reactions, responses, and engagement with enterprise management in Industry 4.0. The “Behavior-oriented drive and influential function of IoTs on Customers in Industry 4.0” concept was used in this study to determine the influence of Industry 4.0 on customers. Results: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function of IoTs on customers. Conclusion: The study concluded that NLP and AI are the preferred system for enterprise management in the era of Industry 4.0 to understand customers’ demands and achieve customer satisfaction. Therefore, NLP and AI techniques are a necessity to attain business goals.

The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers.

Introduction: Recently, vast generational modern AI techniques have facilitated developments for accessing digital healthcare diagnosis with capabilities of detecting illnesses. Problem: There exists a lot of scepticism for e-health couple with high similarities on health symptoms which hinder text data analysis for remote diagnosis limiting remote services and affecting tech development. Objective: This research investigates and substantiates opportunities associated with computational leverage of text data analytics and cognitive extraction of knowledge insights to improve healthcare outcomes. Significance: The study presents an overview of public, an integrated deep learning (DL), and AI knowledge graph (KG) for healthcare accessibility of remote diagnostics with NLP assist. Method: This research applied both qualitative and quantitative analysis. Questionnaires were used to understand the computational analytics and cognitive extraction of AI knowledge graphs on healthcare data. Also, an AI model was built to detect, diagnose based on text data and streamline five (5) related disease symptoms for each given text input. Results: The result of the survey was tested with hypotheses of H1, H2, H3, H4, H5. Results show that deep learning models and knowledge graphs can effectively lead to a well-defined class of data classification. Our model also exhibits a tremendous level of acceptable prediction of health symptoms based on text data. The significant group was accepted as an identified health issue and the non-significant was identified as a non-health issue. Conclusion: The study concludes that a well-defined system based on a rigorous ethical healthcare standard can easily support determining a feasible remote diagnosis.

Deep learning and natural language processing has emerged as modern machine with state-of-the-art modern learning and applicable techniques that help academicians, students, and teachers to identify, evaluate and validate documents and text features. Most educational systems have incorporated deep learning and natural language processing in fulfilling tasks except publishers and editorial boards, whom they handle important aspects of academic achievement in the decision-making for both journals and conferences. The aim is to introduce a standardized systematic peer review process based on factual justification, amendment, and recommendation approach on scholarly works free from emotional sentiments, politicization, bias, criticism, and condemnation. The study uses peer review feedback from journal XXXX to identify, evaluate and validate emotional sentimental aspects. A Confusion Metrix model was examined in the study to justify and validate the emotional sentiment pros and cons of the reviewer. Also, behavior-oriented drive and influential functions were used to convert text to value and rank the sentiment score. Each reviewer's text was converted into value using a behavior-oriented drive and influential function were used to examine the emotional sentiment level of the reviewers. Text mining and extraction of data uses a natural language process approach to identify sentiments from the reviewer's comments. Based on the confusion matrix and behavior-oriented drive, and influential function, the findings revealed limited significant emotional sentiment involved during the peer review process. The study concluded that the reviewers didn't perfectly validate the manuscript content due to minor emotional sentiments. The reviewer's evaluation of paper #Journal XXXX based on the findings exercise, few emotional sentiments were attracted during the review process. The study recommended editorial boards, journals, conferences, and publishing houses implement this approach to avoid emotional sentiments, politicization, and bias impact on academic evaluation, and validation.

Introduction: The rapid changes in computational power of machine learning algorithms and natural language processing applications have led to multi-scale and many core designs in nanotechnology. Machine learning algorithms and natural language processing applications are easing the burden engineers have to go through to understand nanoparticles.Problem: There is still a challenge to predict and control particles of nanomaterials at nanoscale. Aspect-based climatic conditions are negatively impacting the world with huge modification on nanoparticles, nanomaterials and nanostructures.Objective: Study examines aspects of machine learning algorithms and natural language processing applications that can be used to predict and control particles, and structure of nanomaterials at nanoscale. Method and materials. The study examines significance of machine learning algorithms & applications in nanotechnology, examines aspects of machine learning algorithms & natural language processing applications applied in nanotechnology, and discusses current-future trends of nanotechnology based on learning algorithms & natural language processing applications.Results and conclusions. The findings result in the conclusion that machine learning & natural language processing application in nanotechnology is implementing an advanced microscopic revolution with the potential to metamorphose the world's industrialization and scale human existence. Machine learning algorithms have the potential to predict and classify nanomaterials and natural language processing has the potential to retrieve relevant data hidden within the classified nanomaterials which results has a huge significance in the pharmaceutical industry