Suresh Palarimath
@sct.edu.om
Senior Lecturer
University of Technology and Applied Sciences, Salalah
I've authored 25 computer science books, including Database Management Systems, Computer Concepts, and C Programming, in addition to Java and Python programming. In India, I own 5 Indian patents, and over the years, I've published numerous research papers in journals. I have 6 industrial certifications in total. I was the head of the school of computing at the Institute of Business Studies in Papua New Guinea from February 2012 until February 2013. My Lawrence School team was selected to represent INDIA at the 2010 World Robot Olympiad. Currently employed as a Senior Lecturer in Information Technology at the University of Technology and Applied Sciences, Salalah in Oman.
RESEARCH INTERESTS
AI, Machine Learning, Natural Language Processing, IoT
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Suresh Palarimath, Diwakar Chaudhary, K. T. Shivaram, Anil Kumar, Venkata Ramana K. (85599b62-d1b2-4ec5-867d-d4aec5cc8c84, and Jayprakash Vijay
IGI Global
Quantum information processing depends on entanglement, which is very important for safe communication and more computer power. For reliable and scalable quantum transmission, decentralized quantum networks need to make sure that entanglement links are spread out and maintained well. In addition, our way can improve future efforts to distribute entanglement by using machine learning to look at how networks have interacted in the past. Concerns and possibilities of using AI-enhanced entanglement distribution systems on real quantum networks are also looked at. Our method combines artificial intelligence with quantum technologies to come up with new and exciting ways to solve problems and get the most out of quantum computing and communication in distributed systems. Ultimately, the strategic incorporation of AI with quantum computing in decentralized networks, in conjunction with the strategic utilization of artificial intelligence to enhance and regulate the allocation of entanglements in quantum networks, has the potential to bring about substantial disruption.
Suresh Palarimath, Pyingkodi Maran, R. Venkateswaran, Wilfred Blessing N.R, K. V Shiny, and S. Renuga
IEEE
The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence’s framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce.
Suresh Palarimath, Pyingkodi Maran, Wilfred Blessing N.R, T. Sujatha, S. Renuga, and Chithra R.S
IEEE
Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings.
Suresh Palarimath, Pyingkodi Maran, Thenmozhi K, K.V. Shiny, T Sujatha, and Wilfred Blessing N.R
IEEE
Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis.
Pyingkodi M, Parvathavarthini S, Suresh Palarimath, D Deepa, Karthi D, and Maria John Paul M
IEEE
The intake of alcohol and drug consumption among young individuals poses significant societal challenges, necessitating effective preventive measures and early intervention strategies. This study suggests a unique method for predicting youth drug use that makes use of deep learning algorithms and eye gaze movement. This work objective is to use eye gaze movement and deep learning techniques to create a predictive model for young people's use of illicit drugs. The study design involves recruiting a diverse sample of young individuals and collecting their eye gaze data through eye-tracking devices. The collected eye gaze data is preprocessed, segmented, and fed into the deep learning models for feature extraction and prediction. Deep learning algorithms, including ResNet50, VGG19 and Alexnet are employed to extract relevant features and predict the likelihood of drug consumption based on the eye gaze data. The findings of this research highlight the significant relationship between eye gaze movement and illicit drug consumption among young individuals. The developed predictive model shows promising results in identifying high-risk individuals and providing a valuable tool for early intervention and prevention efforts. VGG19 outperforms ResNet 50 and AlexNet with highest accuracy of 0.96. It demonstrates the highest overall performance among the three models for the given task.
Jasim Sharki Ghulam Al Balushi, Malak Ibrahim Ahmed Al Jabri, Suresh Palarimath, Pyingkodi Maran, K Thenmozhi, and C. Balakumar
IEEE
This study analyzes the transformative potential of combining artificial intelligence (AI) with immersive technologies, specifically virtual reality (VR) and augmented reality (AR), to elevate educational outcomes significantly. The synergy of AI and immersive technologies presents a unique opportunity to enhance students’ motivation, memory retention, and comprehension of complex subjects, creating a distinct and impactful learning experience. The research comprehensively explores the current understanding of the effectiveness of VR and AR in educational settings, placing special emphasis on their applications in diverse fields such as Science, Technology, Engineering, And Mathematics (STEM), medicine, language acquisition, and the development of interpersonal skills. Beyond highlighting the immense potential, the study acknowledges and addresses the obstacles that include the costs associated with equipment, training, and content creation. It underscores the importance of collaborative efforts among technology suppliers, educators, and content producers to overcome these challenges effectively. By recognizing and navigating these hurdles, the study aims to provide practical insights for educational institutions and policymakers, advocating for the integration of AI-powered immersive technology to enhance the overall learning experience for students. The findings underscore the transformative impact of such technologies, emphasizing their role in shaping the future of education and preparing students for the dynamic challenges of the modern world.
Suresh Palarimath, Pyingkodi Maran, Thenmozhi K, C. Balakumar, T Sujatha, and Wilfred Blessing N. R
IEEE
The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field.
Pyingkodi M, Parvathavarthini S, Suresh Palarimath, D Deepa, Mohana Prawin. E, and Praveen. R
IEEE
Drug consumption poses significant challenges to public health, demanding precise and timely prediction models for efficient intervention and prevention methods. Through the use of deep learning methods combined with ocular image analysis, this work proposes a novel method for predicting drug use. The objective is to develop reliable and understandable model to recognize drug users from their eye pictures. For supervised training, a varied dataset of eye pictures was gathered, including people with various drug use histories. On the dataset, various Convolutional Neural Network designs were chosen, trained, and optimized using the proper hyperparameters. This study proposal compares various Convolutional Neural Network CNN models, including InceptionV3, DenseNet169, and EfficientNetB2, in order to ascertain which one of these models gets the maximum accuracy. These models provide the average accuracy of 93.33%, 97.47% and 85.89% respectively. DenseNet169 outperforms InceptionV3 and EfficientNetB2 with highest accuracy of 97.47%. It demonstrates the highest overall performance among the three models for the given task
Suresh Palarimath, Pyingkodi M, Thenmozhi K, Mohammed Maqsood, Mohammed Abdul Salam, and Roopa Devi Palarimath
IEEE
In today's modern world, IoT plays an important and multifaceted function in all industries. 5th Generation Wireless Systems - 5G is the primary technical platform on which the Internet of Things plays a major role in smart technologies. Incorporating 5G technology into the architecture of an IoT system is now simple. Using plug-and-play technology, remote access to configuration and control is possible. Smart technology always results in faster data transfer rates, more bandwidth, greater capacity, lower latency, and a quicker output response. Based on this notion, IoT brings about a dramatic shift in heterogeneous networks. So, there is a need to implement 5G-IoT technologies. In the age of the Internet of Things, the fifth-generation (5G) cellular networks give the smart and complex technical environment. 5G Cellular Networks offer great growth potential for IoT technologies. This article gives a thorough study of the role of 5G Cellular Networks in the expansion of IoT technologies across all industries. Since the inclusion of renewable energy emerges as a new trend in the digital world, 5G is very beneficial in the age of the Internet of Things. To achieve the link between 'Things and People,' 'Things and Things,' and 'People and People' in power Systems, it is necessary to combine IoT with 5G in power systems to a high degree of success in various power-based industry-based systems. This article demonstrates a smart scenario and provides a thorough analysis of "how 5G drives the IoT System intelligently in every way and how IoT affects as the "most promising technology" with this smart 5G Wireless Communication System." In addition, the IoT era 5G application possibilities were demonstrated.
Sujatha T., Wilfred Blessing N. R., and Suresh Palarimath
IGI Global
For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome.
Bernard H. Ugalde, Renato R. Maaliw, Suresh Palarimath, Mohammed Bakhit Al Mahri, Albert A. Vinluan, Jennifer T. Carpio, Ace C. Lagman, and Maurine C. Panergo
IEEE
It is always difficult to travel alone in a wheelchair without prior knowledge of the accessibility of the planned route. The majority of people prefer the shorter route. On the other hand, those with ambulatory limitations may prefer a longer route with proper ramps and drop curbs. This study aims to design obstacle management so that a registered user can report the accessibility of a ramp. The research includes an algorithm for generating barrier-free routes on the derived graph paths. When a wheelchair user encounters an obstacle while navigating the suggested route, the algorithm redirects them to their destination. A simulation test was performed, and the entire approach was evaluated using the survey method. The results showed that the proposed routing algorithm could find the shortest paths and reroute users to an unobstructed path. Respondents were highly pleased with the proposed navigation system’s performance and thought it was accessible, usable, and reliable. As a result, the study may provide a novel approach to designing a geographic information system for use in a wheelchair navigation system.
M. Pyingkodi, K. Thenmozhi, M. Karthikeyan, T. Kalpana, Suresh Palarimath, and G. Bala Ajith Kumar
IEEE
Soil fertility is an important factor in determining soil quality as it reflects how well the soil can support plant growth in agriculture. Soil sensor and Arduino can be used to quickly determine the nutrient content of the soil. Nitrogen, phosphorus, and potassium are all considered as important nutrient source components. These components should be measured in order to determine how much extra nutrient content should be added to the soil in order to increase the crop fertility. Soil fertility can be detected by using NPK sensors. Soil nutrient concentration data can help us to determine whether the soil used to support plant production is nutrition deficient or abundant. The nutrient content of the soil samples can be obtained in various ways by using sensing element or mass spectrogram. However, the spectral analysis method is inconvenient, where the records are only 60-70% accurate. By comparing the spectrum analysis method with classic wet chemistry methods, the accuracy of the products needs to be fully resolved due to a scarcity of data. Hence, to detect soil nitrogen, phosphorous, and potassium, a soil NPK sensor should be used. By utilizing a soil NPK sensor, which is of limited cost, fast and easy, elevated, and transportable. Its advantage over a standard detection approach is that it provides extremely fast measurements with accurate data. This paper analyzes and compares different nutrient levels in soil by using kernel density estimation algorithm and machine learning.
M. Pyingkodi, K. Thenmozhi, K. Nanthini, M. Karthikeyan, Suresh Palarimath, V. Erajavignesh, and G.Bala Ajith Kumar
IEEE
The IoT is a new technology trend used in almost every area thing, when connected to the internet and to each other, when you connect to the internet or interconnect, your entire system will be smarter. We have used IoT in all areas of our lives, including smart cities, smart homes, and smart retail. Much more. From 9.6 billion by 2050, agriculture needs to deliver even faster to meet this type of demand. This is possible with the latest technology, especially the IoT. The IoT enables labour free farms. Not only can it be used for large-scale agriculture, but it can also be used for livestock, greenhouse management, and agricultural land management. The most significant tool for the IoT is the sensor. A sensor is a device that collects important data that is interpreted to obtain the required analysis. The important objective of sensors are used to determine the soil's physical qualities and the environment. The main applications of sensors are control and supervise, safety, alarm, diagnostics, and analytics. Sensors make innovative agriculture more effective and trouble-free. In agriculture, the sensor is mainly used for measuring, measuring NPK (Nitrogen, Phosphorus, Potassium) levels, and detecting disease and soil moisture content. The main solution to this problem is smart farming, which modernizes traditional farming practices. This paper narrates the role of IoT application in smart agriculture. Smart farming is also known as precision farming hence it uses accurate information to draw outcomes. It demonstrates the different sensors, applications, challenges, strengths and weaknesses that support the IoT and agriculture.
Suresh Palarimath, N. R. Wilfred Blessing, T. Sujatha, M. Pyingkodi, Bernard H. Ugalde, and Roopa Devi Palarimath
Springer Nature Singapore