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Authors: Revathi Pammaraju

Artificial Intelligence (AI) and Machine Learning (ML) are transforming modern electrical systems by improving efficiency, reliability, safety, and automation. These technologies enable electrical systems to analyze large amounts of data, predict equipment failures, optimize energy consumption, and support intelligent decision-making.

Authors: Dr. Lalitha Nagapuri,Dr. Ch.Navitha, Mr.Bullarao Domathoti, Mr.A.Muni Sankar,Dr.N.DEEPAK KUMAR

Advanced Artificial Intelligence and Machine Learning Techniques provides a comprehensive study of modern Artificial Intelligence (AI) and Machine Learning (ML) concepts, methods, and practical applications. The book begins with the fundamental principles of AI and machine learning and progresses toward advanced algorithms and intelligent computational techniques.

Authors: Dr. Sri Silpa P,Vishnu Prasad Goranthala

Advanced Machine Learning and Artificial Intelligence presents a comprehensive exploration of the principles, techniques, algorithms, and applications that shape modern intelligent systems. The book introduces the foundations of Artificial Intelligence (AI) and Machine Learning (ML), progressing from fundamental concepts to advanced computational approaches.

Authors: Dr.T.Ranjith Kumar,Pala Prathima,Dr.Md Umar Khan,Mrs.R.Akhila

Applied Machine Learning and Artificial Intelligence explores the practical application of intelligent computational methods to solve real-world problems across science, engineering, business, healthcare, education, agriculture, finance, and social development. Machine Learning enables systems to learn patterns from data and improve their performance, while Artificial Intelligence provides broader capabilities such as reasoning, prediction, decision-making, language understanding, computer vision, and intelligent automation.

Authors: Prof .Sreedevi Vallabhapurapu ,Dr.V B Narsimha ,Dr.Ramdas Vankdothu ,Ms Varre Sindhuja

Artificial Intelligence (AI) is transforming engineering by enabling systems to analyze data, make intelligent decisions, predict failures, and optimize performance with minimal human intervention. Artificial Intelligence for Smart Engineering Systems focuses on the integration of AI, machine learning, deep learning, IoT, robotics, and data analytics into modern engineering applications. These technologies help create intelligent, efficient, reliable, and adaptive systems across areas such as manufacturing, energy, transportation, infrastructure, healthcare, and automation.

Authors: Dr.T Ranjith kumar ,Pala Prathima,Dr.MD Umar Khan,Mrs.R.Akhila

No overview summary provided.

Authors: Dr.T Ranjith kumar ,Pala Prathima,Dr.MD Umar Khan,Mrs.R.Akhila

No overview summary provided.

Authors: Dr. Karthik kumar vaigandla ,Dr.Madhu kumar Vanteru,Mrs.Bolla sandhya Rani,Mr.Badugu Ranjith Kumar

The topic describes how Artificial Intelligence (AI) is integrated with electronic systems to make them smarter, more automated, adaptive, and efficient. AI can help electronic devices analyze data, recognize patterns, make decisions, and perform tasks with minimal human intervention.

Authors: Md jalaluddin,Md Abdul Rahman,Mr G Srikanth,G Srinivas,Dr Tarangjni Deshpande

Sustainable materials are environmentally friendly construction materials that reduce resource consumption, waste, and environmental impact throughout a project’s lifecycle. Examples include recycled steel, recycled concrete, bamboo, fly-ash bricks, low-carbon cement, reclaimed wood, and locally sourced materials. Although sustainable materials can have higher initial purchase or installation costs, they can significantly reduce long-term project costs through lower energy consumption, reduced maintenance, improved durability, and less construction waste. Locally available and recycled materials can also reduce transportation and procurement expenses.

Authors: Dr.T.Subha Mastan Rao,Dr.G.Sri Lakshmi ,Dr.S.Jamalaiah, Dr.Naga Bushanam V

Advanced Artificial Intelligence (AI) and Machine Learning (ML) methods are transforming how computers learn from data, recognize patterns, make predictions, and support complex decision-making. Modern approaches include deep learning, reinforcement learning, transfer learning, ensemble learning, natural language processing, computer vision, and generative AI. Deep neural networks enable machines to process large and complex datasets, while reinforcement learning allows systems to improve their decisions through interaction with an environment. Transfer learning reduces training requirements by adapting previously trained models to new applications. Ensemble techniques combine multiple models to improve prediction accuracy and reliability. Generative AI methods, including large language models and diffusion-based models, can generate text, images, code, and other forms of content.

Authors: DR. KBSD SARMA,Gidla Sudheer babu,Gosangi Sukanya ,Kankipati Swathi

Modern Intelligent Electronic Engineering is a comprehensive textbook that introduces the principles, technologies, and applications of modern electronic engineering integrated with intelligent computing systems. It combines traditional electronic engineering concepts with emerging technologies such as artificial intelligence (AI), embedded systems, the Internet of Things (IoT), machine learning, robotics, and smart automation.

Authors: Dr.V.Sesha Ratna kumari,Dr .K.Rajkamal ,Dr.K Anjaneyulu ,Dr.K.Rajkamal

Advanced Intelligent Electronics is a textbook that explores the integration of modern electronics with intelligent technologies such as artificial intelligence (AI), embedded systems, the Internet of Things (IoT), machine learning, and smart control systems. It explains how electronic devices are designed to sense, process, communicate, and make decisions automatically.

Authors: Dr.Rajanikanth Aluvalu ,Dr.Naresh Guduru,Dr.Suresh Saiendra,Mr.S.Phaneendra

Data-Driven Engineering Using Artificial Intelligence provides a comprehensive exploration of how Artificial Intelligence (AI) and data analytics are transforming engineering into a predictive, intelligent, and evidence-based discipline. The book demonstrates how engineering data collected from sensors, simulations, industrial processes, and connected systems can be converted into actionable insights through AI, enabling improved design, optimization, automation, and decision-making.

Authors: Dr.Venkatesh Munagaia,Dr.Nagaraju Sonti, Dr.Rafeeq Mohammed,M.Kiranmai

AI-Based Predictive Maintenance in Engineering Systems: Advanced Strategies and Applications provides a comprehensive guide to the use of Artificial Intelligence (AI) for predictive maintenance in modern engineering and industrial systems. The book explores advanced AI methodologies that enable organizations to predict equipment failures, optimize maintenance schedules, reduce downtime, improve operational efficiency, and extend the lifespan of critical assets.

Authors: Dr.K.B.S.D.Sarma,Bonam Srirama Durga Lakshmi,Mrs.Santhi Chilla,L.Prasanna Devarakonda

Artificial Intelligence: Principles, Architectures, and Intelligent Systems provides a comprehensive and in-depth exploration of the theoretical foundations, architectural frameworks, and practical implementation of Artificial Intelligence (AI). The book presents a balanced approach that combines AI principles with modern system architectures, enabling readers to understand how intelligent systems are designed, developed, deployed, and managed across a wide range of applications.

Authors: DR.Manikyam Sandeep,Dr.Katukojwala Praveen Kumar,Dr.V.Sree Ranganayaki

Advanced Artificial Intelligence Engineering Architectures provides an in-depth exploration of the architectural principles, frameworks, and technologies used to design, develop, deploy, and scale next-generation Artificial Intelligence (AI) systems. The book focuses on engineering robust, intelligent, and scalable AI architectures that integrate machine learning, deep learning, cloud computing, edge AI, distributed systems, and intelligent automation for real-world industrial and engineering applications.

Authors: Dr.B.Narsimha,Dr.G.Sathish Kumar,Dr.A.Chakradhar,Dr.Kiran Kumar M

Engineering in the Age of Artificial Intelligence explores how Artificial Intelligence (AI) is reshaping the engineering profession by transforming the way systems are designed, developed, operated, and maintained. The book provides a multidisciplinary perspective on the integration of AI into engineering, highlighting how intelligent technologies are driving innovation, automation, sustainability, and digital transformation across industries.

Authors: Dr.Ramdas Vankdothu ,Dr.Banothu Naresh ,Mr.Pampati Nagaraju ,Dr.B.Prashanth

Principles of Artificial Intelligence Engineering provides a comprehensive introduction to the core principles, methodologies, and engineering practices required to design, develop, deploy, and manage Artificial Intelligence (AI)-based systems. The book bridges the gap between AI theory and engineering implementation, offering readers a structured understanding of how intelligent algorithms are integrated into modern engineering solutions.

Authors: Dr.Yelisela Rajesh,Rajesh Tulasi,Dr. Satish Kumar Satti,Dr.Kuncham Sreenivasa Rao

Artificial Intelligence in Engineering Applications provides a comprehensive and application-oriented exploration of how Artificial Intelligence (AI) is transforming engineering across multiple disciplines. The book demonstrates how AI techniques—including machine learning, deep learning, computer vision, natural language processing, expert systems, reinforcement learning, and intelligent optimization—are being applied to solve complex engineering problems, improve operational efficiency, and accelerate technological innovation.

Authors: Dr.K.Suneel Kumar,Mr.Yalavarthi Leelakrishna,Mr.Koppisetty R M C Sekhar,Mr.Ahamad Sharif Shaik

AI System Engineering: Principles and Deployment provides a comprehensive guide to designing, developing, deploying, and maintaining Artificial Intelligence (AI)-enabled engineering systems. The book combines the theoretical foundations of AI with engineering best practices, enabling readers to understand the complete lifecycle of AI systems—from problem formulation and data preparation to model development, validation, deployment, monitoring, and continuous improvement.

Authors: Dr. Aparna Joshi,Dr.Sivananda Lahari Reddy,Dr.Mohebbanaaz,Dr.Pannangi Naresh

Smart Engineering Systems with Applied AI provides a comprehensive exploration of how Artificial Intelligence (AI) is transforming traditional engineering systems into intelligent, adaptive, and autonomous solutions. The book focuses on the practical implementation of AI technologies—including machine learning, deep learning, computer vision, natural language processing, reinforcement learning, and intelligent optimization—to improve the performance, reliability, efficiency, and sustainability of engineering systems.

Authors: Vemula Jasmine Sowmya, Dr Burada Sreedhar, K Vinod Kumar Reddy , Dr RadhaKrishna Karne,

Data Mining and Predictive Analytics with Artificial Intelligence explores the integration of AI techniques with data mining to discover hidden patterns, extract valuable insights, and build predictive models from large datasets. It covers machine learning algorithms, data preprocessing, classification, clustering, association rule mining, regression, and deep learning approaches for accurate forecasting and intelligent decision-making. The subject emphasizes real-world applications in healthcare, finance, business, cybersecurity, manufacturing, and smart systems, enabling organizations to make data-driven decisions and improve operational efficiency.

Authors: Dr.P.Kiran kumar,Dr.B.Naveen Kumar,Mrs.k.Madhavi,Dr Karthik Kumar Vaigandla

Electronics and Communication Engineering for Space Technology focuses on the application of electronics, communication systems, and advanced engineering technologies in the development and operation of space-based systems

Authors: Dr.CH N Santhosh Kumar,Dr.Nagesh kumar,D.N ,Dr.Ajay Paithane

Intelligent Engineering with Artificial Intelligence explores the integration of Artificial Intelligence (AI) technologies into modern engineering practices to develop smarter, more efficient, and innovative solutions.

Authors: Dr.Sri Silpa p,Vishnu prasad Goranthala

Advanced Machine Learning and Artificial Intelligence (AI/ML) is an engineering field focused on developing intelligent systems that can learn from data, recognize patterns, make predictions, and solve complex problems. It combines computer science

Authors: Dr.G V.Ramesh Babu,Subbaiahgari R Ajitha ,V.Ramu,L.Sowjanya Upadhyayula

Advanced AI and Machine Learning Techniques explores modern methods that enable computer systems to learn from data, identify complex patterns, make predictions, and perform intelligent tasks. The topic includes deep learning, neural networks, reinforcement learning, natural language processing, computer vision, and generative AI. These techniques are widely applied in robotics, electronic systems, automation, healthcare, cybersecurity, autonomous systems, and smart technologies.

Authors: M N V V K PAVAN KUMAR KAZA ,Dr.E. Suneetha ,Dr.Mucherla Usha rani ,Dr.S.Swathi Rao

achine Learning-Based Artificial Intelligence focuses on how machines use machine learning algorithms and data to learn patterns, make predictions, recognize information, and support decision-making without being explicitly programmed for every task. It covers applications such as image recognition, speech processing, predictive systems, robotics, automation, and intelligent electronic devices

Authors: M N V V K PAVAN KUMAR KAZA ,Dr.E. Suneetha ,Dr.Mucherla Usha rani ,Dr.S.Swathi Rao

achine Learning-Based Artificial Intelligence focuses on how machines use machine learning algorithms and data to learn patterns, make predictions, recognize information, and support decision-making without being explicitly programmed for every task. It covers applications such as image recognition, speech processing, predictive systems, robotics, automation, and intelligent electronic devices

Authors: Dr.Md Umar Khan ,Chukka Samsonu

Artificial Intelligence and Business Management is a comprehensive textbook that examines the transformative impact of artificial intelligence (AI) on modern business strategy, operations, and decision-making. The book provides an in-depth understanding of how AI technologies—including machine learning, generative AI, natural language processing, predictive analytics, computer vision, and intelligent automation—are reshaping organizations across industries.

Authors: Dr.Salendra Suresh,Dr.Ragidi ranadheer reddy ,Dr.Singireddy Mallikarjun reddy, Mrs.V.C.Laxmiprasanna

AI-Driven Electronics: Concepts, Circuits & Applications is a comprehensive textbook that explores the integration of artificial intelligence (AI) with modern electronic engineering, offering readers a practical and theoretical foundation for designing intelligent electronic systems. The book demonstrates how AI technologies are transforming the design, operation, and optimization of electronic circuits and devices across industries such as healthcare, manufacturing, telecommunications, automotive, energy, and consumer electronics.

Authors: G.Sherlin Shobitha ,V.Swetha

Advanced in Artificial Intelligence for Electronic Engineering Applications is a comprehensive textbook that examines the transformative role of artificial intelligence (AI) in modern electronic engineering. The book provides an in-depth understanding of how AI techniques—including machine learning, deep learning, reinforcement learning, computer vision, and intelligent optimization—can be integrated with electronic systems to create smart, autonomous, and data-driven solutions.

Authors: Dr.D Rajendra prasad ,Angothu Govind

AI Intelligent Electronics with AI Technologies is a comprehensive textbook that explores the integration of artificial intelligence (AI) with modern electronic engineering to develop intelligent, adaptive, and autonomous electronic systems. The book presents a multidisciplinary approach that combines electronic circuit design, embedded systems, Internet of Things (IoT), machine learning, deep learning, edge AI, robotics, and intelligent control to address the evolving demands of next-generation technologies.

Authors: Dr.K. Sai Venu prathap,Dr.Kamal Raj, K.Linga murthy

Intelligent Electronic Systems is a comprehensive textbook that introduces the design, analysis, and implementation of electronic systems capable of sensing, processing, communicating, and making intelligent decisions. The book combines the fundamentals of electronics with modern technologies such as embedded systems, artificial intelligence (AI), the Internet of Things (IoT), machine learning, digital signal processing, and automation.

Authors: N.MUSRAT SULTANA

Intelligent Systems Design Using AI and IoT presents a comprehensive guide to designing, developing, and deploying intelligent systems by integrating Artificial Intelligence (AI) with the Internet of Things (IoT). The book explores how connected devices, smart sensors, cloud computing, edge computing, and AI algorithms work together to create autonomous, data-driven systems capable of real-time monitoring, decision-making, and predictive analysis.

Authors: Dr.V.Gayathri,Dr.R.Rajani,Dr.Lakshmana Rao Battarusetty,Dr.Chalama Reddy Tirrireddy

Engineering Intelligence: AI Concepts and Applications provides a comprehensive exploration of Artificial Intelligence (AI) principles and their transformative role in modern engineering. The book combines foundational AI concepts with practical engineering applications, enabling readers to understand how intelligent algorithms and data-driven technologies are solving complex engineering problems across multiple disciplines.

Authors: Dr.G.J.Sunny Deol,Dr.Venkateswara Rao Gera,Dr.G.Manthru Naik,Mr.S.Suresh Babu

Applied AI in Engineering Systems explores the integration of Artificial Intelligence (AI) into modern engineering systems to enhance automation, decision-making, efficiency, and innovation. The book provides a practical approach to understanding how AI technologies—including machine learning, deep learning, computer vision, natural language processing, reinforcement learning, and intelligent optimization—are transforming the design, operation, and management of engineering systems.

Authors: Dr.Ashok kumar Vootla,Mrs.Swathi Jilla ,Dr.V.Sridhar,Dr.Raghunadh pasunuri

Foundations of Artificial Intelligence in Engineering provides a comprehensive introduction to the fundamental principles, concepts, and methodologies of Artificial Intelligence (AI) and their applications in modern engineering. The book establishes a strong theoretical foundation while demonstrating how AI technologies are transforming engineering design, manufacturing, automation, intelligent control, predictive analytics, robotics, and smart infrastructure. Written for undergraduate and postgraduate students, researchers, educators, and engineering professionals, the book explains core AI concepts—including machine learning, deep learning, knowledge representation, search algorithms, expert systems, computer vision, natural language processing, reinforcement learning, and optimization techniques—in a clear and accessible manner. It emphasizes engineering-focused examples, case studies, and practical applications that connect theory with real-world implementation

Authors: Dr.B.Shadaksharappa

Applied Artificial Intelligence for Engineering is a comprehensive guide that explores the practical applications of Artificial Intelligence (AI) across diverse engineering disciplines. The book bridges the gap between theoretical AI concepts and real-world engineering solutions by demonstrating how machine learning, deep learning, computer vision, natural language processing, robotics, and intelligent optimization techniques are transforming modern engineering systems. Designed for undergraduate and postgraduate students, researchers, faculty members, and industry professionals, this book presents AI methodologies with practical case studies, algorithms, and implementation examples. It covers the integration of AI into civil, mechanical, electrical, electronics, aerospace, automotive, manufacturing, industrial, and environmental engineering, helping readers understand how intelligent technologies improve efficiency, reliability, safety, and sustainability. The book also discusses emerging trends such as Industry 4.0, digital twins, autonomous systems, smart factories, predictive maintenance, edge AI, Internet of Things (IoT), explainable AI (XAI), and generative AI, preparing readers for the future of intelligent engineering.

Authors: Dr.Velde Venkateshwarlu ,Dr.Thirupati Battu ,Dr.Pesaru Raju ,Dr.Bandi Krishna

Advanced Data Analytics and Predictive Intelligence Using Artificial Intelligence (AI) is a modern approach that combines data analysis, machine learning, and intelligent algorithms to extract meaningful insights from large and complex datasets and predict future events with greater accuracy. It involves collecting, processing, and analyzing data from various sources such as databases, IoT devices, sensors, cloud platforms, social media, and business applications to identify patterns, trends, and relationships. AI techniques such as machine learning, deep learning, natural language processing (NLP), neural networks, and predictive modeling enable systems to learn from historical data, recognize complex patterns, and generate intelligent predictions. Advanced data analytics helps organizations understand current performance, while predictive intelligence uses AI-driven models to forecast future outcomes, identify potential risks, and support proactive decision-making

Authors: Dr.Pooja Krishnamurthy revankar,Dr.Manish Prajapati ,Dr.Harish H.M

Advanced Data Mining and Predictive Analytics with Artificial Intelligence (AI) is an advanced technology approach that focuses on extracting valuable information from large and complex datasets and using that knowledge to predict future trends, behaviors, and outcomes. Data mining involves discovering hidden patterns, relationships, and useful knowledge from structured and unstructured data using techniques such as classification, clustering, association rule mining, and anomaly detection. Predictive analytics uses statistical methods, machine learning algorithms, and AI models to analyze historical and real-time data to generate accurate forecasts and support intelligent decision-making. AI enhances these processes by automatically learning from data, identifying complex patterns, improving prediction accuracy, and enabling automated analysis without continuous human intervention. Various AI techniques such as supervised learning, unsupervised learning, deep learning, neural networks, natural language processing (NLP), and predictive modeling are used to analyze large datasets and generate meaningful insights

Authors: Dr.Pooja Krishnamurthy revankar ,Dr.Jayprabha VishalKumarTerdale ,Dr.Srinivasarao Udara

AI-Driven Big Data Analytics is the integration of Artificial Intelligence (AI) technologies with Big Data analytics to efficiently process, analyze, and interpret massive amounts of data generated from various sources. It enables organizations to discover hidden patterns, identify relationships, predict future trends, and make intelligent decisions using advanced computational techniques. The main concepts of AI-driven Big Data Analytics include data collection, data preprocessing, data integration, machine learning, deep learning, predictive analytics, real-time data processing, natural language processing (NLP), computer vision, and automated decision-making. AI algorithms help analyze structured, semi-structured, and unstructured data collected from sources such as IoT devices, social media, cloud platforms, sensors, and business applications. Machine learning models learn from historical data to improve predictions, while deep learning techniques identify complex patterns and provide accurate insights from large datasets. AI-driven Big Data Analytics has applications in various fields, including healthcare for disease prediction, medical image analysis, and personalized treatment; finance for fraud detection, risk management, and financial forecasting; retail for customer behavior analysis, recommendation systems, and demand prediction; manufacturing for predictive maintenance, quality control, and smart automation; transportation for traffic management and route optimization; agriculture for crop monitoring and precision farming; and cybersecurity for threat detection and data protection. The major advantages of AI-driven Big Data Analytics include faster data analysis, improved accuracy, intelligent automation, enhanced decision-making, cost reduction, better customer experiences, and increased operational efficiency

Authors: Dr.Omkar Singh ,Anand Kumar Guptha

AI-Driven Big Data Analytics is an advanced approach that combines Artificial Intelligence (AI) techniques with Big Data analytics to process, analyze, and extract valuable insights from extremely large and complex datasets. It focuses on using intelligent algorithms, machine learning models, deep learning techniques, and automated analytical methods to discover hidden patterns, predict future trends, and support data-driven decision-making. The key concepts of AI-driven Big Data Analytics include data collection, data preprocessing, feature extraction, machine learning, predictive modeling, real-time analytics, natural language processing (NLP), computer vision, anomaly detection, and automated decision systems. AI algorithms can analyze structured, semi-structured, and unstructured data generated from various sources such as IoT devices, social media platforms, sensors, cloud applications, business transactions, and digital services

Authors: Dr.Ramdas Vankdothu ,Dr. Velde Venkateshwarlu ,Dr.Pesaru Raju ,Dr.Thirupathi Battu

AI-Powered Big Data Analytics refers to the integration of Artificial Intelligence (AI) techniques with Big Data technologies to analyze massive volumes of structured, semi-structured, and unstructured data and generate intelligent insights. By combining machine learning, deep learning, natural language processing (NLP), predictive analytics, and automation techniques, AI enables organizations to process complex datasets, identify hidden patterns, detect anomalies, and make accurate predictions. Big Data analytics platforms collect information from various sources such as social media, IoT devices, sensors, business applications, cloud systems, and digital transactions, while AI algorithms analyze this data to support faster and smarter decision-making. AI-powered analytics helps organizations perform real-time data analysis, customer behavior prediction, fraud detection, recommendation systems, sentiment analysis, and automated decision-making

Authors: Mrs.Zareena Begum ,Dr.Ayesha banu,Dr.Ayesha Ameen ,Lakshmi Lalitha Sristi

Big Data Analytics is the process of collecting, processing, and analyzing extremely large and complex datasets to discover valuable patterns, trends, and insights that support better decision-making. Big Data is characterized by five major features known as the 5Vs: Volume, Velocity, Variety, Veracity, and Value, which represent the massive amount of data generated, the speed at which data is produced, the different types of data formats, data reliability, and the usefulness of extracted information. Big Data Analytics uses advanced technologies such as data mining, machine learning, artificial intelligence, statistical analysis, cloud computing, and distributed computing frameworks to process large-scale data efficiently. The process involves data collection from sources such as social media, sensors, Internet of Things (IoT) devices, business transactions, and digital platforms, followed by data storage, cleaning, transformation, analysis, and visualization. Organizations use Big Data Analytics to identify customer behavior, predict future trends, optimize business operations, detect fraud, improve decision-making, and create innovative solutions.

Authors: Dr.Naresh Gudur, S.phaneendra ,Dr.N.Meenakshi , P.Udaya bhanu

Artificial Intelligence (AI) in Business Management is the use of advanced technologies that enable organizations to automate tasks, analyze data, improve decision-making, and enhance overall business performance. AI technologies such as machine learning, natural language processing (NLP), predictive analytics, computer vision, and intelligent automation help businesses understand complex information and develop effective strategies. AI plays an important role in various areas of management, including marketing, finance, human resources, customer relationship management, operations, and supply chain management. In marketing, AI helps analyze customer behavior, create personalized recommendations, and optimize advertising campaigns. In finance, AI supports fraud detection, risk management, budgeting, and financial forecasting. In human resource management, AI assists with recruitment, employee performance analysis, and workforce planning. In operations and supply chain management, AI improves production processes, demand forecasting, inventory management, and logistics efficiency. AI-powered business systems can process large amounts of data quickly, identify patterns, predict future trends, and provide valuable insights that help managers make accurate and timely decisions

Authors: Dr.Shyam Kumar,Kathoju Divya Sri ,Assenababu Shaik,Komatireddy Akhila

Artificial Intelligence (AI) for Business Management refers to the application of intelligent technologies that help organizations improve decision-making, automate business processes, optimize operations, and achieve better performance. AI uses technologies such as machine learning, natural language processing (NLP), predictive analytics, robotic process automation, and data mining to analyze large volumes of business data and generate valuable insights. In business management, AI supports managers by providing accurate predictions, identifying market trends, improving resource allocation, and enabling data-driven strategies. AI applications are widely used in customer relationship management, marketing, finance, human resources, supply chain management, and operational planning. In marketing, AI helps businesses understand customer preferences, personalize recommendations, analyze consumer behavior, and improve advertising strategies

Authors: Ragidi Sowjanya,Dr.Rajithu Kotoju

Advanced Cloud Security and Privacy focuses on protecting data, applications, and infrastructure hosted in cloud computing environments from unauthorized access, cyber threats, and privacy risks. As organizations increasingly adopt cloud services for storage, computing, and business operations, ensuring the confidentiality, integrity, and availability of cloud resources has become essential. Advanced cloud security uses technologies and strategies such as encryption, identity and access management (IAM), multi-factor authentication, security monitoring, threat detection, intrusion prevention systems, zero-trust security models, and artificial intelligence-based security analytics to protect cloud environments. Cloud privacy involves managing and safeguarding sensitive information by implementing data protection policies, access controls, compliance frameworks, and secure data handling practices. AI and machine learning techniques are increasingly used in cloud security to detect abnormal activities, identify potential threats, automate security responses

Authors: P.Vaishnavi ,Thandur Vanitha ,Dr.NVSL Narasimham

Artificial Intelligence (AI) in Business Systems and Management refers to the use of intelligent technologies to improve business operations, automate processes, analyze data, and support effective decision-making. AI enables organizations to manage complex business activities by using machine learning, natural language processing (NLP), predictive analytics, robotic process automation (RPA), and data-driven intelligence. In business systems, AI helps optimize workflows, improve customer services, enhance financial management, strengthen supply chain operations, and automate routine tasks. AI-powered systems can analyze large volumes of business data from multiple sources to identify patterns, predict future trends, and provide valuable insights that help managers make accurate and timely decisions. In customer relationship management, AI is used for chatbots, personalized recommendations, customer behavior analysis, and improved service experiences

Authors: Dr.Suneetha Devuluri ,Dr.CH.V.Murali Krishna ,Venugopa lBoppana ,Dr.M.Venkateswara Rao

Artificial Intelligence (AI) is transforming modern business management by enabling organizations to automate processes, analyze large amounts of data, improve decision-making, and enhance operational efficiency. AI technologies such as machine learning, natural language processing (NLP), predictive analytics, computer vision, and intelligent automation help businesses identify trends, understand customer behavior, optimize resources, and develop effective strategies. In business management, AI is used for areas such as customer relationship management, marketing analysis, financial forecasting, supply chain optimization, human resource management, risk assessment, and operational planning. AI-powered systems can analyze historical and real-time data to provide valuable insights, predict market demands, detect fraud, improve productivity, and support managers in making faster and more accurate decisions. In marketing, AI enables personalized recommendations, targeted advertising, and customer sentiment analysis, while in finance it assists with budgeting, investment analysis, and fraud prevention. In human resource management, AI helps in recruitment, employee performance analysis, and workforce planning. AI also improves supply chain management by optimizing inventory levels, predicting demand, and enhancing logistics operations

Authors: Vemula Jasmine sowmya ,Dr Burada Sreeedhar ,K Vinod Kumar Redy ,Dr. RadhaKrishna Karne

Data Mining and Predictive Analytics with Artificial Intelligence (AI) is a powerful approach for extracting meaningful information from large datasets and using that knowledge to predict future outcomes. Data mining focuses on discovering hidden patterns, relationships, trends, and anomalies in structured and unstructured data through techniques such as classification, clustering, association rule mining, and anomaly detection. Predictive analytics builds on these discoveries by applying statistical methods, machine learning algorithms, and AI models to forecast future events, behaviors, and business trends. AI enhances these processes by automating data analysis, learning from historical and real-time data, recognizing complex patterns, and continuously improving prediction accuracy as new information becomes available. The process typically includes data collection, data cleaning, integration, transformation, feature selection, model development, evaluation, and deployment.

Authors: A.Krishna kumar ,Dr.D.Deepika ,S.Vijaya Lakshmi ,Dr.Rajitha Kotoju

Advanced Data Analytics and Predictive Intelligence Using Artificial Intelligence (AI) is the process of analyzing large and complex datasets to uncover meaningful patterns, generate actionable insights, and accurately predict future events and trends. It combines data analytics, machine learning, deep learning, statistical modeling, and AI algorithms to transform raw data into valuable information that supports strategic decision-making. The process begins with collecting data from multiple sources such as databases, sensors, Internet of Things (IoT) devices, social media, cloud platforms, and business applications, followed by data cleaning, integration, transformation, and feature engineering to ensure high-quality analysis. AI techniques such as supervised learning, unsupervised learning, reinforcement learning, neural networks, natural language processing (NLP), and computer vision enable systems to automatically identify hidden patterns, classify information, detect anomalies, and build predictive models that improve continuously as more data becomes available

Authors: Dr.Banoth Ravi Kumar

Smart Analytics, Artificial Intelligence (AI), and Big Data Technologies work together to transform large volumes of raw data into meaningful insights that support intelligent decision-making and business innovation. Smart analytics combines advanced statistical methods, machine learning, data mining, and predictive analytics to identify hidden patterns, trends, and relationships within structured and unstructured data. Big Data technologies provide the infrastructure to store, process, and manage enormous datasets generated from sources such as social media, Internet of Things (IoT) devices, sensors, cloud platforms, business transactions, and mobile applications

Authors: Pavan Subhash Tirumalasetti

Big Data Analytics with Artificial Intelligence (AI) techniques is the process of analyzing massive volumes of structured, semi-structured, and unstructured data to discover meaningful patterns, trends, and insights that support informed decision-making. Big Data is characterized by the five Vs—Volume, Velocity, Variety, Veracity, and Value—which require advanced technologies for efficient storage, processing, and analysis. AI techniques, including machine learning, deep learning, natural language processing (NLP), computer vision, and reinforcement learning, enable systems to automatically learn from data, recognize complex patterns, and generate accurate predictions without extensive human intervention

Authors: Murali Vivekanandam ,Dr.K R N Kiran Kumar,Karthik Kumar Davanam Anjaneya Guptha

Machine Learning (ML) and Artificial Intelligence (AI) play a vital role in modern Big Data systems by enabling organizations to process, analyze, and extract valuable insights from massive volumes of structured and unstructured data. Big Data is characterized by its high volume, velocity, variety, veracity, and value, making traditional data processing techniques insufficient for handling complex datasets. Machine learning algorithms automatically learn patterns from historical and real-time data, while AI enhances these capabilities by supporting intelligent decision-making, automation, and predictive analytics

Authors: Jangam Deepthi ,Dr.M.Madhavilata,Dr.V.Thrimurthulu

Intelligent Data Mining Using Machine Learning and Artificial Intelligence (AI) is the process of discovering meaningful patterns, trends, relationships, and valuable knowledge from large and complex datasets by using advanced AI and machine learning techniques. Unlike traditional data mining, intelligent data mining automatically learns from historical data, improves its performance over time, and makes accurate predictions with minimal human intervention. The process begins with data collection, followed by data cleaning, integration, transformation, and feature selection to prepare high-quality data for analysis. Machine learning algorithms such as decision trees, support vector machines, random forests, neural networks, and clustering techniques are then applied to classify data, detect hidden patterns, identify anomalies, and generate predictive models

Authors: Dr.Omkar singh ,Mr.Navanendra singh,Ms.Abhilasha singh

Applied Artificial Intelligence (AI) in Modern Engineering refers to the use of AI technologies, machine learning, deep learning, computer vision, robotics, and data analytics to improve the design, development, manufacturing, operation, and maintenance of engineering systems. AI enables engineers to analyze large volumes of data, automate repetitive tasks, optimize complex processes, and make faster and more accurate decisions. In mechanical engineering, AI is used for predictive maintenance, quality inspection, and intelligent manufacturing. In civil engineering, AI assists in structural health monitoring, smart city planning, traffic management, and construction project optimization. Electrical and electronics engineering use AI for smart grids, energy management, fault detection, and automated control systems. In computer engineering, AI powers intelligent software, cybersecurity, cloud computing,

Authors: Dr. srikanth Lakumarapu ,Dr.Venkata Ramana Thaduri ,Dr.M.Varaprasad Rao,Dr.Banoth Samya

Advanced Data Mining and Predictive Analytics with Artificial Intelligence (AI) is the process of extracting valuable knowledge from large and complex datasets and using that information to predict future trends, behaviors, and outcomes. Data mining involves identifying hidden patterns, relationships, and useful information from structured and unstructured data through techniques such as classification, clustering, association rule mining, and anomaly detection. Predictive analytics builds on these insights by applying statistical models, machine learning algorithms

Authors: Mr.Allanki Sanyasi Rao, Dr.N.Meenakshi, Dr.Chintam Shravan, Dr.P.Sumalatha

rtificial Intelligence for Data Mining and Big Data Analytics provides a comprehensive introduction to the integration of Artificial Intelligence (AI), data mining, and big data technologies. The book explores how AI techniques such as machine learning, deep learning, natural language processing, and predictive analytics can be applied to extract valuable insights from large, complex, and rapidly growing datasets. It covers both theoretical foundations and practical applications, enabling readers to understand how intelligent systems support data-driven decision-making across various industries.

Authors: Dr.K.SHYAM KUMAR, KATHOJU.DIVYASRI , ASEENABABU SHAIK, KOMATIREDDY AKHILA.

Artificial Intelligence (AI) for Business Management focuses on applying AI technologies to improve organizational decision-making, automate business processes, enhance customer experiences, and drive innovation. It enables businesses to analyze large volumes of data, predict market trends, optimize operations, and increase overall efficiency across various functional areas such as finance, marketing, human resources, supply chain, and strategic planning.

Authors: Dr. Vivek Sharma, Dr. Rajesh Kumar

A cohesive guide to renewable grid designs, photovoltaic storage algorithms, and real-time distribution arrays.