Prof. Ashish Kumar Dass completed his MTech. in Computer Science and Engineering from National Institute of Science and Technology (NIST), Berhampur, Odisha .He is perusing his PhD in Computer Science and Engineering under BPUT, Odisha. He has more than 15 years of experience in Academic and Administration as an Assistant Professor at School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Institute Park, Pallur Hills, Berhampur, Odisha, India. His research interests mainly focus on Under-water Wireless Sensor Network, Ad-hoc & Sensor Network, Computer Security, Artificial Intelligence, image Processing and Mathematical modeling. He is the Life member of ISTE. To his credit he has published more than 15 research paper in national and International conferences, Journals and book chapters.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computational Theory and Mathematics, Multidisciplinary, Multidisciplinary
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Scopus Publications
Scopus Publications
Cloud-based AI Solutions for Smart Engineering Platforms Ashish Kumar Dass, Subratansu Panigrahi, Subhashree Sahu Cognitive Computing with Intelligent Engineering Platforms, 2026 Incorporation of cloud-based Artificial Intelligence (AI) systems into innovative engineering platforms has revolutionized the engineering processes of design, management, and optimization. With AI, ML, and cloud computing, organizations can effectively analyze vast volumes of data, enabling intelligent decision-making, predictive maintenance, and optimized workflows. Cloud services provide the infrastructure needed to deploy AI at scale, enabling real-time data analysis to predict equipment failures, reduce downtime, and allocate resources in response to fluctuating project requirements. The fusion of the Internet of Things (IoT) and cloudbased AI makes clever engineering even smarter, enabling real-time decision-making and thereby optimizing energy use in smart buildings, regulating traffic in smart cities, and improving technological efficiency. Another significant benefit of security is that an AI-powered cloud platform analyzes the network traffic and seeks anomalies and cyber threats autonomously. In addition, the cloud enables engineering teams to collaborate smoothly from different locations using a shared dataset and tools. Cloudbased Artificial Intelligence is transforming modern engineering practices by enabling predictive analytics, dynamic asset management, IoT integration, improved security, and collective innovation. This research paper examines successful instances, citing case studies and best practices that demonstrate the impact of such technologies on Smart engineering.
Blockchain in Industry 4.0 and Industry 5.0: A Paradigm Shift towards Decentralized Efficiency and Autonomous Ecosystems Manjushree Nayak, Asish Panigrahi, Ashish Kumar Dass, Brojo Kishore Mishra, Shashi Kant Gupta Computational Intelligence in Industry 4 0 and 5 0 Applications Trends Challenges and Applications, 2025 Blockchain technology has emerged as a transformative force in the context of Industry 4.0 and is set to play a vital role in the forthcoming Industry 5.0. In the realm of Industry 4.0, blockchain gives a secure platform for decentralized data sharing, enabling efficient supply chain management, smart contracts, and decentralized manufacturing processes. Its core attributes of data integrity, immutability, and trust establish a solid foundation for enhancing operational efficiency and reducing costs. As we transition to Industry 5.0, blockchain’s decentralized nature empowers autonomous systems and the Internet of Things (IoT) to communicate, validate, and execute transactions seamlessly, eliminating the need for intermediaries. This empowers the creation of decentralized marketplaces where machines autonomously trade and share resources, fueling the growth of intelligent and self-regulating ecosystems. The application of blockchain in Industries 4.0 and 5.0 has the potential to revolutionize industries, foster collaboration, and spur innovation, ushering in a connected and highly efficient future.
An AI-based efficient model for the classification of traffic signals using convolutional neural network Manjushree Nayak, Ashish Kumar Dass, Sapna Singh Kshatri Building Secure Business Models Through Blockchain Technology Tactics Methods Limitations and Performance, 2023 The objective of this study is to build a model for the classification of traffic signs available in the image into many categories using a CNN and Keras library to detect the traffic sign. The goal of the traffic sign recognition is to build a deep neural network (DNN), which is used to classify traffic signs. The authors suggest training the model so it can decode traffic signs from natural images using the German Traffic Sign Dataset. This data should be firstly preprocessed in order to maximize the model performance. After choosing model architecture, fine tuning, and training, the model will be tested on new images of traffic signs found on the web. Because it deals with image classification, a convolutional neural network is chosen as a type of DNN, which is a common choice for this type of problem. The code is written in Python with use of tensor flow library. The proposed CNN model identifies traffic signs and classifies them with 95% accuracy. GUI of this model makes it easy to understand how signs are classified into several classes.
Enhanced path planning model for anchor-free distributed localization in wireless sensor networks Tapas Kumar Mishra, Ashish Kumar Dass, Sanjaya Kumar Panda Pdgc 2018 2018 5th International Conference on Parallel Distributed and Grid Computing, 2018 In wireless sensor networks (WSNs), localization stands as a major challenge, which is intended to minimize traveling distance of the beacon node. Moreover, the important issue is to improve coverage and accuracy in the location calculation. In this paper, an improved path planning model for the beacon node is presented, which is capable of node localization using distributed approach. The proposed model focuses to improve coverage of the network topology by moving in zig-zag fashion, so that it will enhance the reachability of message in almost every corner of the deployed area. Further, the model focuses on movement of beacon node in such a way that none of the four neighboring positions of beacon node will be collinear. The proposed model is simulated extensively in a self simulator with different scenarios and compared with SCAN. The tested performance of the model is presented along with its analytical model. The simulation result shows that the proposed model outperforms by 1% to 2% than the existing model in terms of percentage of nodes settled and energy consumption.
An Enhanced Path Planning Model for Anchor-Free Localization in Wireless Sensor Networks Tapas Kumar Mishra, Sitanshu Kumar, Ashish Kumar Dass Proceedings 2018 International Conference on Information Technology Icit 2018, 2018 In Wireless Sensor Network (WSN), localization stands as a major challenge which is intended to maximize with minimized traveling distance of the beacon node. Further, the important issue is to improve coverage and accuracy in calculation of the location. This paper mainly focuses on an enhanced path planning model using beacon node. The proposed model focuses to improve coverage of the network topology by moving in zig-zag fashion so that it will enhance the reachability of message in almost every corner of the deployed area. The proposed model is simulated extensively in a self simulator with different scenarios and compared with SCAN and anchor based model. The tested performance of the model is presented along with its analytical model. The simulation result shows that the proposed model outperforms by 1% to 2% than the existing model in terms of percentage of nodes settled and energy consumption.
A comprehensive performance analysis of energy efficient routing protocols in different traffic based mobile ad-hoc networks B. S. Gouda, A. K. Dass, K. L. Narayana Proceedings 2013 IEEE International Multi Conference on Automation Computing Control Communication and Compressed Sensing Imac4s 2013, 2013 In Mobile Ad hoc network, nodes are connected through a wireless are formed a fast changed network structure and it is a infrastructure less, can be set up anytime, anywhere. The nodes are mobile based on battery operated and nodes have limited battery resources. Routing protocol selection in Mobile Ad Hoc Network is a big challenge, because of its regular topology changes and routing overhead. In Manet, energy efficient protocols are used to forward data packets one to another without much packet loss. Energy reactive routing protocol is effective routing protocol in Manet and nodes are requires energy efficient routing protocols to bound the power consumption, and lengthen the battery life to improve the life time of the network. The main objective of this paper is to enhance the network performance of different routing protocols, when frequent link failure in network due to mobility of the nodes in the network. The performance analysis and simulation are carried out to evaluate network performance using Network Simulator (NS-2), based on the different load, node mobility, delay, packet sending rate and energy consumption. It has been verified through various simulations, which represent a wide range of network conditions that energy AODV deliver the better performance as that of the modern protocols DSDV, TORA, DSDV, DSR and AODV in terms of energy efficiency but it is observed that DSR needs significantly smaller energy overheads than other protocols.
