Monitoring Speaker Sentiment in Various Conditions Using Natural Language Processing

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Ever since the commercialization of the Internet in the '90s, technology has been evolving faster than ever with the advent of cloud computing, social media, ubiquitous mobile devices, the Internet of Things (IoT), blockchain, and more. A staggering number of three billion internet users, five billion mobile users, and six billion devices are now connected through this massive global network of networks, facilitating customer information exchange and interaction never before seen in history. Driven by recent technological advances in computing power, big data, high-speed internet connection, and easier access to models built with advanced algorithms, Artificial Intelligence (AI) is the next wave of innovation, which has already come into widespread awareness in the consumer world with the emergence of virtual assistants and chatbots (e.g., Amazon's Alexa, Apple's Siri, Google's Assistant), image recognition (e.g., Facebook Photos, Google ImageNet), personalized recommendations (e.g., Netflix, Amazon) and autonomous driving (e.g., Tesla, Google Waymo). This qualitative research study intends to learn about the impact of AI on customer relationship management (CRM), specifically in the area of customer service of problem resolution. Most prior research focuses on the AI technologies leveraged in CRM systems, such as machine learning, natural language processing, voice recognition, chatbots, data analytics, and cloud infrastructure. Few extant studies have used a qualitative research methodology to gather data from industry experts to truly understand the impact of AI technologies on customer relationship management, especially in the area of customer service and problem resolution. This study aims to fill this research gap. This research contributes to the literature on AI in the context of CRM and is of value to both academics and practitioners as it provides a detailed analysis and documentation of the impact of AI on the customer service domain.

Artificial Intelligence (AI) is a field of computer science that creates systems capable of simulating human intelligence. Itmeans any technology that seeks to mimic human intelligence, which covers a huge range of capabilities such as voice and image recognition, machine learning techniques,and semantic search.It involves the development of algorithms and computational models that allow machines to learn, reason, make decisions, and solve complex problems autonomously. AI in marketing isthe application of AI technologies like machine learning, natural language processing, and data analytics to optimize and automate marketing tasks. This allows marketers to create more efficient, personalized, and data-driven campaigns.AI in marketing is synonymous with precision, personalization, and efficiency. Marketing AI works by processing data with algorithms and pattern recognition to simulate human intelligence. From chatbots to social media management to full-scale marketing campaigns and automation, artificial intelligence is changing the way marketers do their jobs.In this paper, we will explain various uses of AI in marketing.

Advancing radar-based precipitation nowcasting : an open benchmark and the potential of deep learning

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

Natural language processing is an important direction in the field of computer science and artificial intelligence. It can realize various theories and methods of effective communication between humans and computers using natural language. Machine learning is a branch of natural language processing research, which is based on a large-scale English-Chinese database. Due to the relatively poor alignment corpus of English and Chinese bilingual sentences containing unknown words, machine translation is unprofessional and unbalanced, which is the problem studied in this paper. The purpose of this paper is to design and implement a length-based system for sentence alignment between English and Chinese bilingual texts. The research content of this paper is mainly divided into the following parts. First, the evaluation function of bilingual sentence alignment is designed, and on this basis, the bilingual sentence alignment algorithm based on the length and the optimal sentence pair sequence search algorithm is designed. In this paper, China National Knowledge Infrastructure (CNKI) is selected as an English-Chinese bilingual candidate website and English-Chinese bilingual web pages are downloaded. After analyzing the downloaded pages, nontext content such as page tags is removed, and bilingual text information is stored so as to establish an English-Chinese bilingual corpus based on segment alignment and retain English-Chinese bilingual keywords in the web pages. Second, extract the dictionary from the software StarDict, analyze the original dictionary format, and turn it into a custom dictionary format, which is convenient and better to use the double-sentence sentence alignment system, which is conducive to expanding the number of dictionaries and increasing the professionalism of vocabulary. Finally, we extract the stems of English words from the established corpus to simplify the complexity of English word processing, reduce the noise caused by the conversion of word parts of speech, and improve the operation efficiency. A bilingual sentence alignment system based on length is implemented. Finally, the system parameters are adjusted for comparative experiments to test the system performance.

Artificial intelligence (AI) is a rapidly evolving field that has seen tremendous growth in recent years. In this chapter, the authors provide an overview of current trends in AI and their applications in computer science. They also discuss the future directions of AI research and their potential impact on the field of computer science. They start by introducing the basic concepts of AI and its various subfields. Then, they present an overview of current trends in AI research, including machine learning, natural language processing, computer vision, and robotics. The authors discuss how these trends are applied in computer science, such as autonomous vehicles, fraud detection, and personalized medicine. Finally, they discuss the future directions of AI research, including the development of more explainable AI systems, the integration of AI with other emerging technologies, and the ethical considerations of AI.

In the digital era, Customer Relationship Management (CRM) systems have become central to sustaining competitive advantage through enhanced customer satisfaction and operational efficiency. With the exponential rise in digital interactions, chatbot interfaces powered by Natural Language Processing (NLP) models have emerged as transformative tools in automating and personalizing customer service. This research explores the design, integration, and evaluation of chatbot user interfaces (UIs) tailored for CRM using advanced NLP techniques. The study begins with a comprehensive review of existing literature, highlighting the evolution of chatbots from rule-based systems to intelligent, context-aware virtual assistants. Key NLP components—such as intent recognition, entity extraction, sentiment analysis, and context management—are discussed in the context of enhancing user interaction and personalization. We then present a modular architecture for chatbot-CRM integration, emphasizing user interface design principles that prioritize accessibility, trust, and conversational clarity. A comparative analysis of popular chatbot platforms (Salesforce Einstein Bot, Azure Bot, Rasa, and Dialogflow) is conducted based on criteria such as NLP capabilities, CRM integration, analytics support, and cost-effectiveness. Real-world performance metrics—including customer satisfaction (CSAT), average resolution time, and escalation rates—are analyzed to assess the effectiveness of chatbotdriven CRM solutions. Furthermore, we identify key challenges such as language ambiguity, handling complex queries, and ethical concerns like data privacy and AI bias. The paper concludes with strategic recommendations for hybrid models combining AI and human support, and future research directions including emotion-aware chatbots and multilingual support. This study provides a roadmap for businesses and developers to implement effective, scalable chatbot UIs that optimize customer relationship management using NLP.

Customer Relationship Management (CRM) platforms are undergoing a fundamental transformation driven by rapid advancements in Artificial Intelligence (AI). Modern CRM systems are no longer limited to transactional data storage or rule-based automation; instead, they are evolving into intelligent, adaptive ecosystems capable of predictive decision-making, autonomous workflows, and real-time personalization. This paper examines future trends shaping AI-driven CRM ecosystems, with a particular focus on machine learning, generative AI, autonomous agents, real-time analytics, and ethical AI governance. The study analyzes emerging architectural patterns, evolving use cases, and enterprise challenges associated with scalability, trust, and regulatory compliance. By synthesizing academic research and industry practices, the paper proposes a future-ready AI-CRM ecosystem model that supports intelligent customer engagement, operational efficiency, and strategic decision-making. The findings highlight how AI will redefine CRM from a supporting system into a core digital intelligence layer for enterprises. Artificial Intelligence (AI) is rapidly transforming Customer Relationship Management (CRM) platforms from transactional systems into intelligent, predictive, and autonomous enterprise ecosystems. Modern CRM solutions are no longer limited to data storage and workflow automation; instead, they are evolving into AI-driven decision platforms capable of real-time personalization, predictive analytics, and autonomous service orchestration. This paper explores future trends in AI-driven CRM ecosystems with a focused analysis on leading enterprise platforms such as Salesforce, Microsoft Dynamics 365, and SAP CRM. The study examines architectural evolution, embedded AI capabilities, data integration patterns, ethical considerations, and emerging technologies such as generative AI, autonomous agents, and industry-specific CRM intelligence. By synthesizing platform capabilities and future directions, this paper provides a structured framework for understanding how AI will shape next-generation CRM systems.

The application of Artificial Intelligence (AI) to Customer Relationship Management (CRM) is transforming the way businesses interact with their customers and grow their revenue on a sustainable basis. This article offers a conceptualization to understand the strategic change in CRM with the advent and development in AI technologies in machine learning, natural language processing, predictive analytics, and intelligent automation. Built on Resource-Based View and Dynamic Capabilities Theory, the framework posits that the development of AI-CRM systems goes through four layers: data integration, intelligence generation, customer engagement, and feedback-driven optimization.

The CoViD-19 pandemic generated huge quantities of healthcare and clinical data. Artificial intelligence (AI) may help in processing such data, to support patient care, and to plan healthcare actions for pandemic control. This paper aims at analyzing the state of the art of AI and Clinical Information Systems to support the management of CoViD-19 patients. The analysis is performed according to a proposed taxonomy based on methodologies and techniques, and it also discusses some research directions.We consider and extend some recent taxonomies, for classifying intelligent information systems and Artificial Intelligence techniques for data-intensive applications. We consider methodologies and techniques for CoViD-19 pandemic data analysis.We describe the state of the art, according to the proposed taxonomy, i.e., data collection, machine learning, natural language processing, process mining and pathway identification, decision support systems.We analyze and highlight some emerging directions for short- and mid-term research activities.

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.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information, and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024.

Chatbot has become an innovation for business to connect with customers. People can communicate with technology devices in the same way they would with real people through Chatbots. Traditional Chatbots typically depend on already programmed principles and replies, while AI (artificial intelligence) Chatbots comprehend and dynamically respond to user inquiries utilizing natural language processing (NLP) and machine learning (ML). Chatbots powered by AI have an increased requirement to communicate with customers in a fast and effective way. People can communicate with technology devices like they were speaking to a real person, which are software programs that simulate human conversation which can be texts or speeches. Many criteria quantitative criteria, including frequently asked questions (FAQs), security, brands, improving efficiency, and enhancing engagement, etc., and quantitative criteria, including cost, bot analytics built-in templates and customer relationship management (CRM), etc., need to be considered when evaluating AI Chatbots for companies to enhance customer service. Moreover, criteria may have different importance. Therefore, evaluating AI Chatbots is a MCDM (multiple criteria decision making) problem. Many companies do not know how to select the most suitable one to serve their customers. To address this issue, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), one of MCDM approaches, is used to evaluate AI Chatbots; and criteria weights will be produced by applying BWM (Best Worse Method). A numerical example will be used to present feasibility of the used method, and a comparison will be conducted to display its effectiveness.