Advancements in Artificial Intelligence and Machine Learning
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- Title
- Copyright
- End User License Agreement
- Contents
- Preface
- List of Contributors
- Next-Gen Mechatronics: The Role of Artificial Intelligence
- Nafees Akhter Farooqui1, Zulfikar Ali Ansari1,*, Rafeeq Ahmed2, Ahmad Neyaz Khan1, Shadab Siddiqui3, Mohammad Ishrat1, Mohd Haleem4 and Sarosh Patel5
- INTRODUCTION
- OVERVIEW OF ARTIFICIAL INTELLIGENCE
- Machine Learning (ML)
- Deep Learning (DL)
- Natural Language Processing (NLP)
- Computer Vision
- Robotics
- Expert Systems
- Data Science
- Explainable AI
- APPLICATIONS OF AI IN MECHATRONICS
- Robotics
- Self-driving Vehicles
- Smart Manufacturing
- Healthcare
- CHALLENGES IN AI-MECHATRONICS INTEGRATION
- Multidisciplinary Coordination
- Handling Complexity
- Real-time Processing
- Sensor Fusion
- Robustness and Adaptability
- Safety and Reliability
- Data Efficiency and Privacy
- Integration with Legacy Systems
- Data Availability
- Safety and Reliability
- Ethical Considerations
- FUTURE PROSPECTS
- Explainable AI
- Cognitive Mechatronics
- Swarm Robotics
- CONCLUSION
- REFERENCES
- Advancements and Applications of Artificial Intelligence and Machine Learning: A Comprehensive Review
- Santoshachandra Rao Karanam1,*, A.B. Pradeep Kumar1, Prakash Babu Yandrapati1, B. Pruthviraj Goud2, S. Vijaykumar2 and Illa Mahesh Kumar Swamy2
- INTRODUCTION
- BACKGROUND
- OBJECTIVES
- STRUCTURE OF THE PAPER
- EVOLUTION OF ARTIFICIAL INTELLIGENCE
- Early Developments
- Emergence of Machine Learning
- Deep Learning Revolution
- PRINCIPLE CONCEPTS IN ARTIFICIAL INTELLIGENCE
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning Architectures
- Natural Language Processing
- Computer Vision
- RECENT ADVANCEMENTS IN AI AND ML
- RECENT ADVANCEMENTS IN NLP AND COMPUTER VISION
- APPLICATIONS OF ML IN AI
- APPLICATIONS OF AI IN HEALTHCARE
- AI IN FINANCE
- TRANSPORTATION AND AUTONOMOUS SYSTEMS
- AI IN ENTERTAINMENT AND GAMING
- CHALLENGES AND ETHICAL CONSIDERATIONS
- FUTURE DIRECTIONS AND OPPORTUNITIES
- CONCLUSION
- REFERENCES
- AI-based Aging Analysis of Power Transformer Oil
- Mohammad Aslam Ansari1, Mohd Khursheed2,* and M. Sarfraz3
- INTRODUCTION
- PROPERTIES OF TRANSFORMER OIL
- Electrical Properties
- Electrical Breakdown Voltage (BDV)
- Resistivity
- Dielectric Dissipation Factor (Tan Delta)
- Physical Properties
- Water Content
- Interfacial Tension
- Flash Point
- Viscosity
- Pour Point
- Chemical Properties
- Neutralization Value
- Corrosive Sulphur
- BASICS OF "ANN" AND "ANFIS" METHODS
- DEVELOPMENT OF ANN MODEL FOR AGE PREDICTION OF OIL
- Simulation Results of "ANN Model
- DEVELOPMENT OF "ANFIS" MODEL FOR AGE PREDICTION OF OIL
- Simulation Results of "ANFIS" Model
- COMPARISON OF "ANN" AND "ANFIS" MODEL
- CONCLUSION
- REFERENCES
- Artificial Intelligence and Social Media: Strength, Management and Responsibility
- Nafees Akhter Farooqui1,*, Shamsul Haque Ansari1, Mohd Haleem2,*, Rafeeq Ahmed3 and Mohammad Islam2
- INTRODUCTION
- Communication and Connectivity
- Information Dissemination
- Cultural Impact
- Political Impact
- Economic Influence
- Mental Health
- Privacy and Security
- THE ROLE OF SOCIAL MEDIA AND ITS IMPACT ON SOCIETIES
- DIFFERENT WAYS THROUGH WHICH SOCIETIES CAN BE MANIPULATED
- Social Media and Online Platforms
- Stories and Edited Video Content
- Political Interference
- Dissemination of False Information
- Manipulation Through Deepfakes
- Monetary Policies
- ESSENTIAL CHARACTERISTICS OF MANIPULATION
- Deception
- Control
- Exploitation
- Planning with a Strategic Approach
- Exerting an Impact on Emotions
- MANIPULATION AND AI
- ADDRESSING DIGITAL MANIPULATION
- Critical Thinking and Media Literacy
- Verify Information
- Check URLs and Sources
- Be Skeptical of Emotional Appeals
- Update Privacy Settings
- Use Strong Passwords and Enable Two-Factor Authentication
- Stay Informed About Digital Threats
- DISINFORMATION DETECTION AND COMBATING DISINFORMATION
- ETHICAL OBLIGATIONS AND SOCIETAL RESPONSIBILITIES OF AI DEVELOPERS
- Transparency and Responsibility
- Equity and Impartiality
- Privacy
- Security
- Empowering Users
- Evaluation of Societal Repercussions
- Ongoing Observation and Enhancement
- Cooperation and the Exchange of Knowledge
- SOCIETAL RESPONSIBILITIES OF REGULATORY BODIES
- Implementation of Criteria
- Safeguarding the Rights and Interests of Consumers
- Ensuring the Safety of the Public
- Ethical Reflections
- Promotion of Knowledge and Consciousness
- Engagement with Global Organizations
- CONCLUSION
- REFERENCES
- Recent Trends in AI-Driven Human Detection Tactics
- Mohd. Aquib Ansari1, Khalid Anwar1,*, Arvind Mewada1 and Aasim Zafar2
- INTRODUCTION
- CLASSIFICATION OF HUMAN DETECTION TECHNIQUES
- CLASSIFIERS
- Naive Bayes Classifier (Generative Learning Model)
- Nearest Neighbor
- Logistic Regression (Predictive Learning Model)
- Decision Trees
- Random Forest
- Neural Network
- DATASETS FOR HUMAN DETECTION
- FUTURE RESEARCH OPPORTUNITIES
- Exploring Fuzzy Logic in Human Detection
- Neutrosophic Deep Learning Architectures for Multimodal Human Detection
- Adaptive Fusion of Fuzzy and Neutrosophic Techniques
- Explainable AI for Human Detection
- Cross-Domain Transfer Learning with Fuzzy and Neutrosophic Models
- Combating Cyber Attacks in Human Detection System
- CONCLUSION
- REFERENCES
- A Review of Sentiment Analysis Opinion Mining and Using Machine Learning
- Nadiya Parveen1,* and Mohd Waris Khan1
- INTRODUCTION
- Sentiment Analysis
- Sentiment Analysis Applications
- Role of Machine Learning in Sentiment Analysis
- REVIEW OF LITERATURE
- COMPARATIVE ANALYSIS
- Methods and Approaches Used for Sentiment Analysis
- MACHINE LEARNING TECHNIQUES
- Naïve Bayes (NB)
- Support Vector Machine (SVM)
- Decision Tree (DT)
- Dataset Domain
- Challenges of Sentiment Analysis
- CONCLUSION AND FUTURE SCOPE
- REFERENCES
- State-of-the-Art Techniques in Visual Analysis for Image Processing and Pattern Recognition: A Systematic Review
- Santoshachandra Rao Karanam1,*, Naresh Tangudu2, Kalangi Praveen Kumar3, T.N.S. Padma4, Illa Mahesh Kumar Swamy3 and P. Nagamani3
- INTRODUCTION
- Overview of Image Processing and Pattern Recognition
- Importance
- Applications
- FUNDAMENTALS OF IMAGE PROCESSING
- Basics of Digital Images
- Image Representation (Pixel, Colour Models)
- Image Enhancement Techniques
- Histogram Equalization
- Contrast Stretching
- Filtering (Spatial and Frequency Domain)
- Image Restoration
- Image Compression
- Lossless Compression
- Lossy Compression
- Image Transform
- IMAGE SEGMENTATION
- Thresholding Techniques
- Edge Detection
- Region-based Segmentation
- Clustering Techniques
- Watershed Transform
- FEATURE EXTRACTION
- Basics of Feature Extraction
- Feature Selection Methods
- Texture Analysis
- Shape Analysis
- Feature Descriptors (SIFT, SURF, etc.)
- PATTERN RECOGNITION
- Introduction to Pattern Recognition
- Supervised and Unsupervised Learning
- Classification Techniques
- Support Vector Machines (SVM)
- Decision Trees
- Neural Networks
- k-Nearest Neighbors (k-NN)
- Performance Evaluation Metrics
- OBJECT DETECTION AND RECOGNITION
- Object Detection Techniques
- Haar Cascades
- Histogram of Oriented Gradients (HOG)
- Object Recognition
- Template Matching
- Deep Learning-based Approaches
- Applications in Computer Vision
- CASE STUDIES AND APPLICATIONS
- Medical Image Processing
- Biometric Recognition
- Remote Sensing
- Autonomous Vehicles
- Security and Surveillance
- CHALLENGES AND FUTURE DIRECTIONS
- Current Challenges in Image Processing and Pattern Recognition
- Emerging Technologies
- Potential Future Trends
- CONCLUSION
- SUMMARY OF KEY POINTS
- IMPORTANCE OF IMAGE PROCESSING AND PATTERN RECOGNITION
- FINAL REMARKS
- CONSENT FOR PUBLICATION
- REFERENCES
- Cyber-Physical Architecture of Smart Grid Network
- A.K.M. Ahasan Habib1, Mohammad Kamrul Hasan1,* and Shayla Islam2
- INTRODUCTION
- POWER GRID DEVELOPMENTS
- DIFFICULTIES OF CONVENTIONAL GRID
- SMART GRID
- SMART GRID KEY TECHNOLOGY
- ENVIRONMENT AND ECONOMIC IMPACT
- CONCLUSION
- ACKNOWLEDGEMENTS
- REFERENCES
- Improving the Hardware Security of Wireless Sensor Network Systems by Using Soft Computing
- Masood Ahmad1,*, Mohd Waris Khan1, Satish Kumar1, Mohd Faizan1, Mohd Faisal1, Malik Shahzad Ahmad Iqbal2 and Raees Ahmad Khan3
- INTRODUCTION
- MATERIAL AND METHOD
- Step 1: Hierarchical Structure
- Step 2: Pairwise Comparison
- Step 3: Calculate Priority Weights
- Step 4: Consistency Check
- Step 5: Synthesize Results
- Step 6: Decision Making
- RESULTS AND DISCUSSION
- Step 1: Relationship
- Step 2: Normalization
- Step 3: Calculate Priority Weights for Criteria
- Step 4: Calculate Consistency
- Step 5: Synthesize Results
- Hardware Encryption
- Step 6: Decision Making
- Final Weighted Score for "Secure Boot" (S1)
- Final Weighted Score of TPM (S2): 0.144
- Final Weighted Score of Physical Lock (S3): 0.463
- Final Weighted Score of Hardware Encryption (S4): 0.555
- CONCLUDING REMARKS
- REFERENCES
- Unveiling the Sky: Exploring Synergies in Drone Robotics and Automation through Artificial Intelligence and Machine learning
- Md Akhtar Khan1,*, Kiran Kumar2 and Ahmed F. EI Sayed3
- INTRODUCTION
- ARTIFICIAL INTELLIGENCE IN DRONE SYSTEM FOR VARIOUS APPLICATION
- Drones for Military Purposes
- Drones for Disaster Management
- Drones for Healthcare Delivery
- Agricultural Drones
- MACHINE LEARNING APPLICATIONS IN DRONE SYSTEMS
- INTEGRATION OF AI ML AND DRONE AUTOMATION
- Challenges and Future Directions
- CONCLUSION
- ACKNOWLEDGEMENTS
- REFERENCES
- An Expert System-Assisted AI Approach for Awareness and Prevention of Crimes against Women in India
- Niranjan Panigrahi1,*
- INTRODUCTION
- BACKGROUND INVESTIGATION
- Crimes against Women and Indian Penal Code: A Brief
- Related Works
- PROPOSED APPROACH
- Preliminaries
- Rule-set and KB
- Inference Engine
- IMPLEMENTATION AND TESTING
- Implementation
- Testing
- CONCLUSION AND FUTURE WORK
- ACKNOWLEDGEMENT
- REFERENCES
- EfficientNet B0 Model Architecture for Brain Tumor Detection and Classification Using CNN
- Vendra Durga Ratna Kumar1,*, Fadzai Ethel Muchina1, Md Muzakkir Hussain1 and Priyanka Singh1
- INTRODUCTION
- Problem Statement
- Challenges Associated with Traditional Approaches to Brain Tumor Classification
- Literature Review
- Methodology
- Dataset Description
- Preprocessing
- Data Acquisition
- Noise Reduction Techniques
- Correction of Artifacts
- Enhancement of Contrast and Improvement of Resolution
- EfficientNetB0
- Proposed Layers
- Limitations
- Training
- Evaluation Metrics
- Experimental setup
- Metrics for Evaluating Performance
- Results and Analysis
- CONCLUSION AND FUTURE WORK
- REFERENCES
- Subject Index
Next-Gen Mechatronics: The Role of Artificial Intelligence
Nafees Akhter Farooqui1, Zulfikar Ali Ansari1, *, Rafeeq Ahmed2, Ahmad Neyaz Khan1, Shadab Siddiqui3, Mohammad Ishrat1, Mohd Haleem4, Sarosh Patel5
1 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India
2 Department of CSE, Government Engineering College, West Champaran, Kumarbagh, Bihar, India
3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad-500075, Telangana, India.
4 Department of Computer Science, Era University, Lucknow, Uttar Pradesh 226003, India
5 School of Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Abstract
The incorporation of artificial intelligence (AI) into healthcare systems has demonstrated significant potential to transform patient care, diagnosis, and treatment. Nevertheless, the implementation of artificial intelligence (AI) in the healthcare sector presents difficulties concerning transparency, interpretability, and trust, especially when there are new possibilities for automated decision-making and enhanced efficiency in many different areas, thanks to the combination of artificial intelligence and mechatronics. Automation and robotics are improving as mechatronics integrates AI. Grand View Research expects the global mechatronics and robotics course market to reach $3.21 billion by 2028, expanding 13.7% from 2021 to 2028. This chapter aims to give a general outline of mechatronics-related artificial intelligence (AI), including its applications, advantages, and challenges. The field focuses on developing intelligent machines with the ability to learn, understand data, and react accordingly. Machine learning and deep learning are two forms of artificial intelligence that have enabled robots and autonomous vehicles to detect their environment, traverse complicated scenarios, and make smart decisions using the data they collect. Artificial intelligence (AI) improves mechatronic systems by expanding their capabilities, which boosts their performance, output, and reliability. Nevertheless, ethical considerations and implementation challenges need to be resolved before the full potential of AI in mechatronics can be realized.
Keywords: Artificial Intelligence, Deep learning, Machine learning, Mechatronic, Robots.* Corresponding author Zulfikar Ali Ansari: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India; E-mail: zulfi78692@gmail.com
INTRODUCTION
The primary objective of mechatronics is to build intelligent systems through the integration of several disciplines, including electronics, control engineering, computer science, mechanical engineering, and mechanical engineering. It is a young and expanding area that has already made a big splash in many sectors, including robotics, manufacturing, aerospace, healthcare, and automobiles. In the development of cutting-edge technology and novel approaches to difficult challenges, mechatronics is an indispensable tool. The Japanese invented the word "mechatronics" in the late 1960s, fusing the mechanical "mecha" with the electrical "tronics" [1].
It arose in reaction to the growing need for systems and products to incorporate both mechanical and electronic parts. Intelligent machines that are precise, efficient, and adaptable in their work are the goal of mechatronics.
The remarkable adaptability and versatility of mechatronic systems are attributed to their capacity to perceive and react to their surroundings. To accomplish complicated tasks independently or with little to no human involvement, these systems are programmed to communicate with one another, with other machines, and with the real environment. They can detect, analyze, and respond to data because of the sensors, actuators, microcontrollers, and algorithms built into their software.
Everything from basic home appliances and cell phones to advanced industrial robots and driverless cars falls under the umbrella of mechatronics. When it comes to making sure these systems work, are reliable, and are safe to use, mechatronic engineers are the ones to call. The capacity of mechatronics to unite several branches of engineering is one of its main strengths. More efficient, dependable, and cost-effective systems can be created by mechatronics engineers by integrating mechanical, electrical, and computer engineering principles [2]. By bringing together experts from different fields, we can improve performance and functionality by integrating hardware and software components seamlessly.
Innovation and technological progress are propelled by mechatronics. It makes possible the creation of state-of-the-art technology including smart systems, automation, robotics, and artificial intelligence. In addition to enhancing productivity, security, and quality of life, these technologies may cause a revolution in several different industries [3].
Hence, mechatronics is an interdisciplinary discipline that integrates electrical engineering, control engineering, computer science, and mechanical engineering to develop intelligent systems. Because it facilitates the creation of cutting-edge technology and novel solutions, it has grown into an important field in many different sectors. When it comes to developing flexible and versatile systems, mechatronics experts are crucial in combining software and hardware components [4]. I am confident that mechatronics will revolutionize engineering and our daily lives thanks to its capacity to spur innovation and technical progress.
OVERVIEW OF ARTIFICIAL INTELLIGENCE
The field of Artificial Intelligence (AI) is ever-evolving as scientists work tirelessly to develop increasingly intelligent and powerful machines. Over the past few years, advancements in artificial intelligence (AI) have completely altered our daily lives and the way we accomplish collective goals. An extensive review of AI, including its background, current uses, difficulties, and possible future advancements, will be presented in this essay [5]. Artificial intelligence has been around for a long time; in fact, machines that look like humans first appeared in ancient tales and folklore. In contrast, computer scientists began investigating the possibility of developing computers with intelligence comparable to that of humans in the 1950s, marking the beginning of the contemporary era of AI development. The inaugural use of the term "artificial intelligence" was during the 1956 Dartmouth Symposium, when researchers deliberated on developing intelligent robots [6]. Fig. (1) shows just an overview of Artificial Intelligence.
Creating expert systems and rule-based systems that could simulate human decision-making was the primary goal of early artificial intelligence research. Unfortunately, data shortages and insufficient computer capacity caused progress to be slow. A lot of data was available and machine learning techniques came out in the 1990s, but AI didn't take off until then [7]. The term "artificial intelligence" describes computers that can learn, reason, and make judgments just like a person. Two main schools of thought exist within the field of artificial intelligence: narrow AI and general AI. Narrow AI is purpose-built to excel in a small subset of general AI activities. However, the goal of general AI is to make machines as smart as humans are in a variety of contexts. The widespread use of AI is revolutionizing many different industries and bringing about significant gains in productivity. The healthcare industry is seeing a surge in the use of artificial intelligence. Medical data can be analysed by machine learning algorithms to aid in drug discovery, forecast patient outcomes, and identify disorders. The use of AI-powered robots in surgery has also been found to increase accuracy and decrease the likelihood of human mistakes [8].
Fig. (1))Overview of artificial intelligence.
Autonomous vehicles are being reshaped by artificial intelligence in the transportation industry. To assess their surroundings, make decisions, and makeovers safely, self-driving cars employ artificial intelligence algorithms. Better and more environmentally friendly transportation may be possible with the help of this technology if it can lessen traffic jams, accidents, and carbon emissions. The banking sector is...
