Archives

2024

Vol 9, No 2 (2024): Ai and Machine Learning in Predictive Maintenance for Instrumentation Systems

Author: Dr. Ramesh Tiwari

Abstract: Predictive maintenance leverages AI and machine learning to minimize downtime and operational costs in instrumentation systems. This paper explores predictive models trained on sensor data to forecast equipment failures. Results demonstrate a substantial reduction in unplanned downtimes and increased equipment lifespan.

Keywords: Predictive maintenance, AI, machine learning, sensor data, instrumentation.

Vol 9, No 2 (2024): Optimization of Pid Controllers Using Machine Learning Techniques

Authors: Priya Mehta, Prof. Suresh Gupta

Abstract: PID (Proportional-Integral-Derivative) controllers are widely used in industrial control systems, but tuning these controllers can be a complex and time-consuming process. This paper investigates the application of machine learning techniques to optimize PID controller parameters for various industrial processes. The proposed methods include genetic algorithms, neural networks, and reinforcement learning, which aim to automate the tuning process and improve controller performance. Case studies in process control and robotics demonstrate the effectiveness of these machine learning-driven optimizations.

Keywords: PID Controllers, Machine Learning, Genetic Algorithms, Neural Networks, Tuning Optimization

 

Vol 9, No 2 (2024): Application of Fuzzy Logic in Robust Control Systems

Authors: Rahul Sharma, Neha Verma, Rohit Singh

Abstract: Fuzzy logic has emerged as a powerful tool for dealing with uncertainties in control systems, especially in environments where precise mathematical models are unavailable. This paper explores the application of fuzzy logic in designing robust control systems for industrial instrumentation. The use of fuzzy controllers in processes such as chemical plants, robotics, and automotive systems is discussed, along with their advantages over traditional control methods. Several case studies highlight the role of fuzzy logic in improving system adaptability and resilience.

Keywords: Fuzzy Logic, Robust Control, Uncertainty, Industrial Systems, Adaptability

Vol 9, No 2 (2024): Analytical Approaches To Sensor Fusion in Industrial Control Systems

Authors: Dr. Amitabh Desai, Prof. Seema Patil

Abstract: Sensor fusion has become an integral part of modern industrial control systems, where data from multiple sensors are combined to create a more accurate and reliable system response. This paper discusses the analytical methods used for sensor fusion, including Kalman filters, Bayesian networks, and machine learning techniques. The paper highlights how sensor fusion improves decision-making processes in control engineering by reducing noise and uncertainty in sensor data. Practical applications in fields like robotics, autonomous systems, and manufacturing processes are provided to illustrate the advantages of different fusion techniques.

Keywords: Sensor Fusion, Kalman Filters, Bayesian Networks, Machine Learning, Industrial Control

Vol 9, No 2 (2024): Advancements in Model Predictive Control for Nonlinear Systems

Authors: Prof Priya Nair, Dr. Anil Saxena

Abstract: This paper explores the recent advancements in Model Predictive Control (MPC) for nonlinear systems, particularly in industrial instrumentation and control engineering. The focus is on improving stability, robustness, and computational efficiency in real-time applications. The paper reviews several modified MPC algorithms, highlighting their impact on performance in controlling complex nonlinear processes. Several case studies in chemical, automotive, and aerospace industries are used to demonstrate the effectiveness of these advanced MPC methods. The importance of sensor fusion, real-time data processing, and optimization techniques is also discussed to address future challenges in nonlinear system control.

Keywords: Nonlinear Systems, Model Predictive Control, Real-Time Optimization, Robustness, Industrial Applications.

Vol 9, No 1 (2024): Ethical AI: Navigating the Moral Landscape of Artificial Intelligence

Authors: Vishal Kapoor, Ananya Mukherjee

Abstract: As artificial intelligence continues to advance, the ethical implications of its development and deployment become increasingly important. This paper explores the ethical challenges and considerations associated with AI, including fairness, accountability, transparency, and privacy. We analyze case studies where AI has both positively and negatively impacted society, highlighting the importance of ethical guidelines and regulations. The paper also discusses frameworks for developing ethical AI, such as human-centered design and value-sensitive design. Finally, we propose future directions for ethical AI research, emphasizing the need for interdisciplinary collaboration and the inclusion of diverse perspectives.

Keywords: Ethical AI, Fairness, Accountability, Transparency, Privacy

Vol 9, No 1 (2024): Progress towards Artificial General Intelligence

Authors: Geeta Gupta, Rohini Rathore

Abstract: Artificial General Intelligence (AGI) represents a leap in AI research, aiming to create machines with cognitive abilities comparable to humans. Unlike narrow AI, which excels in specific tasks, AGI seeks to understand, learn, and apply knowledge across a broad range of domains. This paper explores the evolution of AGI, from early concepts to the current state of research, highlighting key advancements, challenges, and future directions. It discusses interdisciplinary approaches, the importance of scalability and generalization, ethical considerations, and the potential societal impacts of AGI. The paper concludes by emphasizing the need for continued research, ethical frameworks, and collaboration to realize the promise of AGI.

Keywords: Artificial General Intelligence, AGI, machine learning, cognitive architectures, neural-symbolic systems, scalability, transfer learning, ethics, neuromorphic computing

 

Vol 9, No 1 (2024): Leafscan: AI Assistant for Farmers

Authors: V. P. Kharge, Shreya Sadanand Dalwai, Madhavi Sanjay Gurav, Shraddha Uttam Jong, Sejal Arvind Mane

Abstract: LeafScan: AI Assistant for Farmers is a groundbreaking project aimed at revolutionizing agriculture by empowering farmers with advanced technology. This project combines a web application and a chatbot, termed "Farmers Assistant," to provide farmers with a comprehensive solution for identifying and managing plant diseases. Utilizing Python, LeafScan employs a deep learning model tailored for classifying plant diseases based on user-uploaded images. The model, derived from deep learning techniques, preprocesses images and offers the top five predictions with associated class labels, probabilities, and confidence scores, ensuring accurate disease identification. LeafScan also aims to enhance disease detection capabilities through the development of a Convolutional Neural Network (CNN) model. Additionally, the project emphasizes the importance of a user-friendly interface, utilizing HTML, CSS, and JavaScript, to facilitate seamless interaction for farmers. Furthermore, the Farmer's Assistant chatbot is poised to provide holistic support beyond disease identification, catering to farmer queries and offering comprehensive agricultural assistance. LeafScan stands at the forefront of leveraging AI to address real-world challenges in agriculture, promising significant advancements in crop management and sustainable farming practices.

Keywords: LeafScan, AI Assistant, Plant Diseases, Deep Learning Model, Farmers Assistant

Vol 9, No 1 (2024): Impact of Industry 4.0 Technologies on Environmental Awareness and Sustainability

Author’s: Dr. Meenakshi, Karumanchi Brundhakshitha, Abhishek, Dr. Soumyalatha

Abstract: This paper investigates how Industry 4.0 technologies contribute to environmental sustainability in today’s industry. It introduces key Industry 4.0 technologies, emphasizing their role and the importance of environmental awareness. The article outlines how these technologies advance sustainability. A concise literature review summarizes key findings and improvements needed in this area. This work explores the environmental impact of Industry 4.0 technologies using case studies and empirical evidence. It also highlights data analytics and machine learning for sustainable decision-making, along with energy efficiency and resource optimization in manufacturing, referencing quantifying studies. The study delves into Industry 4.0’s role in a circular economy and waste reduction, by addressing implementation challenges and opportunities. Concluding with a brief discussion on emerging trends and their implications for sustainability, the article underscores the ongoing relevance of Industry 4.0 in promoting environmental awareness and sustainability.

Keywords: Sustainable decision-making, Circular economy, Machine Learning, Green manufacturing, Resource optimization, Energy-efficient processes, Smart production systems, Food supply chain, Sustainable Development Goals, over engineering.

Vol 9, No 1 (2024): Artificial Intelligence in Textile Industry-Review

Author:Prof (Dr) Nemailal Tarafder

Abstract:Artificial Intelligence is an important application in the development of enterprises, having most essential part to play in taking advantages of independent operational efficiency. One of such areas of artificial intelligence is the textile industry that has seen proper growth. AI is a computer generated system to imitate human intelligence processes. Textile industry has a great future in manufacturing by the application of AI With the application of in textile industry it has been possible to produce smart clothing using IoT and electronic sensors. In the present scenario of industry the new technology is the beginning to change the prospect of textile industry. AI has not only improved the efficiency in industry but also overall industry operations. AI covers a vast area of applications. With the help of AI designs identify trends, optimise patterns and generate 3D model garments. Revolutionary change in textile industry may come by the application of AI in the near future.

Keywords-operational efficiency, pattern making, quality products, machine learning, smart clothes, innovative textiles


2023

Vol 8, No 1 (2023): Artificial Intelligence Advancements, Applications, and Ethical Considerations

Authors:Gaurav Saxena

Abstract:Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various industries and aspects of our daily lives. This paper provides an overview of AI, its advancements, applications across diverse domains, and the ethical considerations surrounding its implementation. It explores the underlying concepts of AI, including machine learning, neural networks, and natural language processing, while discussing the potential benefits and challenges associated with AI adoption. Additionally, the paper examines key ethical concerns such as bias, privacy, and the impact of AI on the workforce. Finally, it concludes with a discussion on the future of AI and the importance of responsible development and deployment.

Keywords:Artificial Intelligence, AI, ethics, bias, fairness, privacy, data security, accountability, transparency, employment, social equality, algorithmic transparency, responsible AI, ethical considerations.

Vol 8, No 1 (2023): Prescription Reader Using Machine Learning: An Analysis

Aurhors:  Manjusha Tatiya, Neel Chitre, Anmol Dhage , Neha Sharma

Abstract: Prescription reading is an important task in the healthcare industry, as it helps to ensure that patients receive the correct medications and dosages. However, manual prescription reading can be time-consuming and error-prone, leading to potential harm for patients. Machine learning has the potential to automate this task, improving efficiency and accuracy. In this paper, we review the state-of-the-art in prescription reading using machine learning techniques, including support vector machines, deep learning, and recurrent neural networks. We also propose a novel approach using convolutional neural networks for handwritten prescription recognition.

Keywords:  Machine Learning, Handwritten Prescription, Medications and Dosages


Vol 8, No 1 (2023): Thyroid Detection using Machine Learning

Authors:  Diksha Tardekar, Swayam Suryavanshi, Vaishnavi Chavan, Sagar Chavan

Abstract: Thyroid diseases are affecting millions of people globally, and timely detection is crucial for effective treatment. However, traditional methods such as blood tests and ultrasounds have limitations in terms of accuracy, cost, and availability. In recent years, machine learning techniques have shown promise in thyroid detection by leveraging large datasets and advancements in computational power. This paper reviews state-of-the-art machine learning techniques for thyroid detection, including classification, clustering, and deep learning methods. We discuss the challenges and limitations of existing approaches, such as data imbalance, interpretability, and model generalization. Additionally, we highlight potential future research directions, including the integration of multi-modal data, explainable AI, and personalized medicine.

Keywords: Machine Learning, Thyroid Detection, Thyroid Detection Using Machine Learning

Vol 8, No 1 (2023): Pixie – A Speech and Gesture Enabled Virtual Assistant

Authors:  Rahul Patil , Sandip Chavan , Ameya Bhupendra Deodhar , Chinmayi Kamalakar Juikar , Pradnya Sham Jagtap, Samruddhi Murlidhar Jadhav

Abstract: In today’s pace forward generation, it is convenient and functional to make daily tasks automated and digitized. Digitization opens many possibilities to make our repetitive tasks easier using assistive technology. Artificial assistants make use of machine learning, artificial intelligence and natural language processing to provide a personalized and conversational experience. This paper discusses about Pixie which is a desktop voice assistant aiming to provide a personalized, interactive and secure experience. Pixie also has face recognition system and is gesture enabled. Voice assistants are an emerging technology with a great future. In this paper, we discuss the development of a voice assistant in desktop.

Keywords:  Artificial Intelligence, Virtual Assistant, Natural Language Processing, Machine Learning.

Vol 8, No 1 (2023): Textile and Artificial Intelligence-A Review

Authors: Dr. Nemailal Tarafder

Abstract: The term artificial intelligence (AI) is associated to any machine that exhibits traits related to a human mind such as learning and problem solving. For the manufacturing, artificial intelligence is reshaping their production process and the way they conduct business. Textile is such an industry where a wide variety of technologies can be applied to advance processes and provide fresh new varieties of clothing fabrics and fibres.

 

Textile manufacturing businesses with access to historical and real-time operational data can leverage artificial intelligence to improve efficiency and augment the capabilities of their human employees. Automated inspection can be performed by the use of artificial intelligence and image processing for inspection of the quality of the product. Based on the improvement of the machine vision theory, industry can use industrial robot technology to realize the automation of cut pieces cutting and replace manual labour. Artificial intelligence is the field of study that deals with the synthesis and analysis of computational agents that act intelligently. One area of apparel manufacturing where artificial intelligence improves quality control is grading of yarn and other base materials. Automation in garment product is becoming a reality due to technical development and the use of modelling and simulation. Artificial intelligence is one of the technologies that can help in extensive data management as it uses the gained information from big data machine to do things that once were the human domain.

Keywords: Artificial intelligence, smart textiles, new technologies, fabric inspection, sewing technology, digital components, quality control.


2022

Vol 7, No 2 (2022): Utilizing Android Studio as Part of a Tourism Enhancement Strategy Based on Artificial IntelligenceUtilizing Android Stu

Authors:- Kusum Singh, Dr. Pooja Malhotra

Abstract:- Artificial intelligence is a relatively recent technique in computer science and information technology. Artificial intelligence techniques are extremely beneficial and are used in a wide range of organizations. In the subject of artificial intelligence, there are several study topics and prospects. The simulation of human intellect in computers is referred to as "artificial intelligence. Artificial intelligence enables app developers to provide better mobile app experiences while also improving tailored options for consumers. This article investigates the many applications of an artificial intelligence-based system in tourism. This report outlines critical future research directions.

Keywords:- Mobile App, Android Development, Artificial Intelligence, Computers.

Vol 7, No 2 (2022): Unmanned Aerial Vehicle Situational Awareness Operations for Remote Route Planning and Gesture Control

Authors:- Ahmad Siddiqui, Uma Qureshi

Abstract:- Unmanned Aerial Vehicles (UAV) has made their way into modern modes of transportation and communication opening a whole new world to be explored. Used for various purposes inclusive of scouting, security, cinematography and delivery systems, they have changed the way in which we use the air as a medium. The Paper proposes a Nano Autonomous Drone which devices deep Reinforcement Learning for SNR (Search and Rescue) in an indoor environment and GUI based path planning for outdoor environment. The Project encompasses a low-fledged object detection system for UAV scouting, face tracking model, hand-gesture detection and control of UAVs, a simple and efficient GUI for path planning for scouting using UAVs. The connectivity to the UAV here is enabled through WIFI and is programmed on a python backend and uses Node to send and receive information from the UAV. When used with the autonomous navigation unit, this allows the drone to be propelled without collision. These practices as proposed enable UAVs to be used effectively for SNR. Keeping in mind the concept of indoor navigation and minimum hindrance, the concept includes a nano drone with a decent processing and control unit. Models thus hosted on the same are low-fledged with a decent efficiency so as to achieve the best out from the smallest. This paper proposes a semi-autonomous nano drone which helps to save people’s lives without risking the lives of the first responders.

Keywords:- Reinforcement Learning, UAVs, Transfer Learning, Search and Rescue.


Vol 7, No 2 (2022): Enabling Digital Transformation Future through AI

Authors:- Deepti Sharma

Abstract:- Artificial intelligence is not just about robots that have authority over humans, but also about how they operate together. Machines offer humans with insight and perspective, but they do not play the key function of providing judgement and creativity. In this day and age, artificial intelligence has a wide range of applications. The confluence of human ingenuity with technology produces in enthusiasm that can tackle different world problems and obstacles.

Keywords:- Artificial Intelligence, Machine learning, Technology, Digital transformation.

Vol 7, No 2 (2022): A Short Review of Artificial Intelligence

Author:- Rasel Hossain

Abstract:- In today's world, we live in a cybernetic society. Technology has done just that. Today there has been a lot of progress; computers are faster and smarter than 20 years ago. Artificial Intelligence (A.I.) is a multidisciplinary field that aims to automate the activities that currently require human intelligence. Today machines have excelled in many ways, today the use of (A.I.) has made the task much easier and simpler. In a short time (A.I.) is giving us a lot of work and solving our various problems, For example, Fraud detection, virtual customer support (VCA), medical science, aviation, banking, financial institutions, gaming, entertainment, etc. So, we can say that without (A.I.) our survival has become impossible.

Keywords:- Artificial Intelligence, Virtual Customer Support, Computers, Cybernetic Society

Vol 7, No 2 (2022): Review Paper on Classroom Monitoring System Using Artificial Intelligence

Authors:- Tushar Patil, Amol Kanwade, Nikita Patil, Pro f.Pooja Belagali

 

Abstract:- The Classroom management system in project work focuses on the project work method and the teacher's leader role in collage. The main reason for focusing on this work method is that we must continuously learn and develop to keep up with societal developments.

The article Classroom management in project work focuses on the project work method and the teacher's leader role in a lower secondary school. The main reason for focusing on this work method is that we must continuously learn and develop to keep up with societal developments.

Keywords:- Classroom Management, Teacher as Leader, Project Work, Qualitative Study.

Vol 7, No 1 (2022): Analysis of Air Quality in Urban Area using Machine Learning Approach

Authors: Sakshi S. Suroshe, S. V. Dharpal

Abstract: Air Quality security has gotten one of the foremost fundamental exercises for the administration in numerous mechanical and concrete zones in today’s world. The meteorological and traffic factors, consuming crude oil derivatives and mechanical parameters perform critical jobs in air contamination which make an adverse effect on living beings. With this expanding pollution on the earth, we also had different executing models that can record data about centralizations of air pollutants (SO2, NO2, etc.). The affidavit of those unsafe gases is noticeable all around; is influencing the character of individuals’ lives, particularly in urban territories. Of late, numerous specialists began to study about this concern and mentioned several measures to manage these conditions with the assistance of the presidency and native people. Data Analytics is a leading approach as it includes natural detecting systems and sensor information accessible. Machine Learning strategies are utilized to predict the ratio with relation to other components present in the earth’s atmosphere. Various regression models are used to predict the air quality and their relative effects.

Keywords: Air Quality Index, Machine Learning, Air Pollutants, Adverse Effects, Parameters, Air Quality prediction, Air Pollution, Linear Regression, Regression Analysis.

 

 

Vol 7, No 1 (2022): Recognition and Solution for Handwritten Equation Using Convolutional Neural Network

Authors: Asmita Ghosh, Vanshika, Harshvardhan Kharpate

Abstract: In recent years, the recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. The diversity of approaches to the problem and the lack of a commercially viable system, however, indicate that there is still much research to be done in this area. In this thesis, I will describe an on-line approach for solving a handwritten mathematical expression. For classification of specific characters we apply Convolutional Neural Network. Each of the correct detection, character string operations is used for the solution of the equation.  Finally the experimental results show the great effectiveness of our proposed system.

Keywords: Handwritten, Convolutional Neural Network, Pattern Recognition, Proposed system

Vol 7, No 1 (2022): Prediction of COVID-19 Using Genetic Deep Learning in Keras

Authors: Samruddhi Ghunake, Madhuri Gorade, Snehal Nalawade, Pranali Jeure, G.S.Navale, S.H. Lokhande

Abstract: Rapid spread of Coronavirus disease COVID-19 leads to server pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster and cheaper. This study aims to provide a solution for identifying pneumonia dur to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method.

Keywords: Genetic Deep Learning Convolutional Neural Network (GDCNN), Computed Tomography (CT), Chest X-Ray (CXR), Artificial Intelligence (AI)

Vol 7, No 1 (2022): Image based Plant Leaf Disease Identification by Support Vector Machine Learning Technique

Authors: Anju Rani, Dr. Avinash Sharma

Abstract: Artificial Intelligence offers vast opportunities for application in agriculture; there still exists a lack of familiarity with high tech machine learning solutions in farms across most parts of the world. AI systems also need a lot of data to train machines and to make precise predictions. Tomatoes (Solanum lycopersicum) can be grown on almost any moderately well-drained soil type. This research presents an image based plant leaf disease identification by support vector machine learning technique. Simulation is performed using Python sypder 3.7 version. The overall accuracy is achieved 98% in different plant leaf disease identification.

Keywords: Sypder, Python, Accuracy, AL, Plant, Disease, Machine Learning.    

Vol 7, No 1 (2022): Crime Prediction Using Machine Learning Approach

Authors: Suyash Koule, Shreyash Kudache, Saksham Dhere, Aniket Keru, N.V Shaha

Abstract: Crime is one of the serious issues in our society. It is the most predominant aspect of our society. It is also predominant in society. So, the prevention of crime is one of the important tasks. The crime analysis should be done in a systematic way as the analysis makes it important in the detecting and prevention of crime. The analysis detects the investigating patterns and helps in the detection of trends in crime. The main of this paper is the analysis of the efficiency of the crime investigation. The model is designed for the detection of crime patterns from inferences. The inferences are collected from the crime scene, and these inferences, the paper demonstrates the prediction of the perpetrator. The paper gives the research way for the prediction of perpetrator age and gender. This paper gives two major aspects of crime prediction. One is perpetrator gender, and the other is perpetrator age. The parameters used are analysis of the various factors like the year, month, and weapon used in the unsolved crimes. The analysis part identifies the number of unsolved crimes. The prediction task involves the description of the perpetrator's age, sex, and relationship with the victim. The dataset used in this paper is taken from the Kaggle. The system predicts the output using Multilinear regression, K-Neighbor's classifier, and neural networks. It was trained and tested using a machine learning approach. 

Keywords: Crime Prediction, KNN, Decision Tree. Multilinear Regression; K-Neighbors Classifier, Artificial Neural Networks.


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