Ajit Kumar Sahoo

@kiit.ac.in

ASSISTANT PROFESSOR-I, School of Computer Appications, KIIT University
Kalinga Institute of Industrial Technology

Ajit Kumar Sahoo


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https://researchid.co/ajitsahoocs

EDUCATION

Ph.D. in Computer Science from University of Hyderabad

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Computer Science, Artificial Intelligence, Computer Science Applications
14

Scopus Publications

222

Scholar Citations

6

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Novel Counterfactual Recourse Generation Using Causality-Guided Feature Perturbation
    Krishna Sameer, Jashmin Swain, Ajit Kumar Sahoo
    Communications in Computer and Information Science, 2026
  • Caption Generation for Sensing-Based Activity Using Attention-Based Learning Models
    Bhabanisankar Pati, Ajit Kumar Sahoo, Siba K Udgata
    IEEE Sensors Letters, 2024
    In recent years, sensing systems have been extensively used for motion detection, activity detection, and gesture recognition, among a few other important applications. Wearable sensors, such as smartwatches and smartphones, contain accelerometers and gyroscope sensors that sense a user's movements and activities to detect abnormal events. Inspired by recent breakthroughs in neural machine translation and the generation of image descriptions, we propose a first-of-its-kind novel attention-based encoder–decoder model to generate a caption to summarize various activities detected for a period from smartphone sensor data. The proposed model architecture consists of three layers: 1) the bidirectional long short-term memory (BiLSTM) incorporates both past and future information from the raw sensor data, then generates features; 2) an attention mechanism is used to assign different weights depending on the feature importance; and 3) an LSTM layer is used generate a sequence of activities, and then, a caption generator module is used to generate the caption. The performance of the proposed model is evaluated using two widely used public datasets (UCI-HAR and WISDM) and one experimental dataset. The model achieves good accuracy on UCI-HAR and our experimental dataset compared to WISDM dataset. The proposed model is able to achieve an average word error rate of 8.20%, accuracy of 90.75% with the UCI-HAR dataset, and an average word error rate of 10%, as well as accuracy of 90% with our experimental dataset.
  • Material Classification based on Non-contact Ultrasonic Echo Signal Using Deep Learning Approach
    Ajit Kumar Sahoo, Siba K. Udgata
    Procedia Computer Science, 2024
    Non-contact ultrasonic sensors are generally used for range measurement and object detection. In addition to the shape and size of the target object, the identification of the material types plays a vital role in robotic navigation and autonomous vehicle applications. Ultrasonic echo signals contain a significant amount of information and can be used to recognize and categorize different materials. Echo signals can help robots detect the obstacle's material on the path and comply with its behavior accordingly. The emergence of deep neural networks has shown great promise, offering cutting-edge performance for a wide range of signal-processing tasks. This work uses the non-contact ultrasonic echo signal from a set of materials (glass, wood, metal plate, sponge, and cloth) for classification. The main idea is to classify the materials using the embedded information of reflected echo signals. Hilbert transform is used to get the envelope from the raw echo ultrasonic signal. In this paper, a novel architecture for one-dimensional convolutional neural network (1D-CNN) has been proposed to accomplish the classification task. The CNN model takes raw echo signals as input to detect and classify materials accurately. The performance of the classifier model is evaluated using accuracy, precision, recall, and F1-score. The proposed 1D-CNN deep learning-based multi-class classifier model can classify the different types of materials with an accuracy of 96%, precision of 95%, recall of 95%, and F1-score of 95%.
  • Wi-SafeHome: WiFi Sensing Based Suspicious Activity Detection for Safe Home Environment
    Gayathri Gorrepati, Ajit Kumar Sahoo, Siba K. Udgata
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
  • Wi-Fi Sensing based Real-Time Activity Detection in Smart Home Environment
    Ajit Kumar Sahoo, Vaishnavi Kompally, Siba K Udgata
    Apscon 2023 IEEE Applied Sensing Conference Symposium Proceedings, 2023
    Wi-Fi sensing technology is being used extensively for different sensing applications, mostly for human activity recognition in the recent past. The Channel State Information (CSI) identifies the frequency shifts in the wireless medium caused by movements and changes in the region of interest, which can be analyzed using the amplitude data of various frequency channels. However, the existing systems are not suitable for real-time implementation due to the three layer cloud based architecture used. With IoT devices, edge computing provides a suitable solution with negligible communication latency and reduced network traffic. The proposed work mainly focused on real-time human activity information extraction in smart home environments using CSI values of low-cost ESP32 WiFi device. The center point of this work is to implement the IoT and edge layers of the three layered architecture to extracts, processes and visualize the sensing data in real-time with low-latency. We use simple statistical features and light weight machine learning algorithms for human activity recognition in real-time instead of complex and computationally heavy algorithms which is not suitable for edge computing.
  • WiFi Sensing Model for Intrusion Detection in Smart Home Environment
    Gayathri Gorrepati, Ajit Kumar Sahoo, Siba K Udgata
    IEEE Region 10 Humanitarian Technology Conference R10 Htc, 2023
    Recently, WiFi signals are not only used for communication purposes but also for various sensing applications. The Channel State Information (CSI) of the received WiFi signal captures the environmental dynamics. This CSI data can be used for motion detection, gesture recognition, localization, human presence detection, environmental monitoring, and other device-free sensing applications. The amplitude and phase of CSI data give fine-grained information about the changes in the signal transmission path due to disturbances caused by physical and environmental changes. This paper focuses on the monitoring of intrusion-related activity by using low-cost WiFi-enabled ESP32 micro-controller devices. We performed four different intrusion-related human activities in the indoor environment. Performed feature engineering on received CSI data for all the activities such as entering into the room, leaving the room, sneaking into the room without entering, and doing suspicious activities in front of the room. We used a Low-pass filter to filter out the noise from the received signal. To classify the activities accurately, we used different lightweight machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). The result shows that the KNN performs better than other models with an accuracy of 95 %.
  • WiFi Sensing Model for Intrusion Detection in Smart Home Environment
    Gayathri Gorrepati, Ajit Kumar Sahoo, Siba K Udgata
    IEEE Region 10 Humanitarian Technology Conference R10 Htc, 2023
    Recently, WiFi signals are not only used for communication purposes but also for various sensing applications. The Channel State Information (CSI) of the received WiFi signal captures the environmental dynamics. This CSI data can be used for motion detection, gesture recognition, localization, human presence detection, environmental monitoring, and other device-free sensing applications. The amplitude and phase of CSI data give fine-grained information about the changes in the signal transmission path due to disturbances caused by physical and environmental changes. This paper focuses on the monitoring of intrusion-related activity by using low-cost WiFi-enabled ESP32 micro-controller devices. We performed four different intrusion-related human activities in the indoor environment. Performed feature engineering on received CSI data for all the activities such as entering into the room, leaving the room, sneaking into the room without entering, and doing suspicious activities in front of the room. We used a Low-pass filter to filter out the noise from the received signal. To classify the activities accurately, we used different lightweight machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). The result shows that the KNN performs better than other models with an accuracy of 95 %.
  • A novel fuzzy inspired machine learning framework for relative humidity estimation using time-of-flight of ultrasonic sensor
    Ajit Kumar Sahoo, Siba K. Udgata
    Measurement Journal of the International Measurement Confederation, 2022
  • Wi-Fi Signal-Based Through-Wall Sensing for Human Presence and Fall Detection Using ESP32 Module
    Sahoo Ajit Kumar, K. Akhil, Siba K. Udgata
    Lecture Notes in Networks and Systems, 2022
  • Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor
    Ajit Kumar Sahoo, Siba K. Udgata
    Lecture Notes in Networks and Systems, 2021
  • A Novel ANN-Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring
    Ajit Kumar Sahoo, Siba Kumar Udgata
    IEEE Transactions on Instrumentation and Measurement, 2020
  • Heat transfer characteristics of an 180° bend pipe of different cross sections using nano-enhanced ionic liquids (NEILs)
    Hemanta K. Pradhan, Ajit K. Sahoo, Manmatha K. Roul, Mohamed M. Awad, Ashok K. Barik
    SN Applied Sciences, 2020
  • Learning to detect SQLIA using node centrality with feature selection
    Debabrata Kar, Ajit Kumar Sahoo, Khushboo Agarwal, Suvasini Panigrahi, Madhabananda Das
    International Conference on Computing Analytics and Security Trends Cast 2016, 2017
  • Codon degeneracy and amino acid abundance influence the measures of codon usage bias: improved Nc (N̂c) and ENCprime (N̂′ c) measures
    Siddhartha Sankar Satapathy, Ajit Kumar Sahoo, Suvendra Kumar Ray, Tapash Chandra Ghosh
    Genes to Cells, 2017

RECENT SCHOLAR PUBLICATIONS

  • Novel Counterfactual Recourse Generation Using Causality-Guided Feature Perturbation
    K Sameer, J Swain, AK Sahoo
    International Conference on Computing, Communication and Learning, 459-471 , 2025
    2025
  • Material classification based on non-contact ultrasonic echo signal using deep learning approach
    AK Sahoo, SK Udgata
    Procedia Computer Science 235, 606-616 , 2024
    2024
    Citations: 13
  • Caption generation for sensing-based activity using attention-based learning models
    B Pati, AK Sahoo, SK Udgata
    IEEE Sensors Letters 8 (3), 1-4 , 2023
    2023
    Citations: 3
  • Wi-safehome: Wifi sensing based suspicious activity detection for safe home environment
    G Gorrepati, AK Sahoo, SK Udgata
    International Conference on Intelligent Human Computer Interaction, 291-302 , 2023
    2023
    Citations: 3
  • Wi-fi sensing based real-time activity detection in smart home environment
    AK Sahoo, V Kompally, SK Udgata
    2023 IEEE Applied Sensing Conference (APSCON), 1-3 , 2023
    2023
    Citations: 22
  • WiFi Sensing Model for Intrusion Detection in Smart Home Environment
    Gayathri Gorrepati, Ajit Kumar Sahoo and Siba K. Udgata
    2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), 329-334 , 2023
    2023
    Citations: 3
  • A novel fuzzy inspired machine learning framework for relative humidity estimation using time-of-flight of ultrasonic sensor
    AK Sahoo, SK Udgata
    Measurement 195, 111035 , 2022
    2022
    Citations: 5
  • Wi-fi signal-based through-wall sensing for human presence and fall detection using esp32 module
    S Ajit Kumar, K Akhil, SK Udgata
    Intelligent Systems: Proceedings of ICMIB 2021, 459-470 , 2022
    2022
    Citations: 20
  • Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor
    AK Sahoo, SK Udgata
    Next Generation of Internet of Things: Proceedings of ICNGIoT 2021, 657-668 , 2021
    2021
    Citations: 1
  • A Novel ANN Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring
    AK Sahoo, SK Udgata
    IEEE Transactions on Instrumentation and Measurement 69 (6), 3359-9456 , 2019
    2019
    Citations: 108
  • Codon degeneracy and amino acid abundance influence the measures of codon usage bias: improved Nc ( N̂ c ) and ENCprime ( N̂ ′ c ) measures
    SS Satapathy, AK Sahoo, SK Ray, TC Ghosh
    Genes to Cells 22 (3), 277-283 , 2017
    2017
    Citations: 36
  • Codon usage bias is not significantly different between the high and the low expression genes in human
    SS Satapathy, SK Ray, AK Sahoo, T Begum, TC Ghosh
    Int. J. Mol. Genet. Gene Ther 1 (1) , 2015
    2015
    Citations: 8

MOST CITED SCHOLAR PUBLICATIONS

  • A Novel ANN Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring
    AK Sahoo, SK Udgata
    IEEE Transactions on Instrumentation and Measurement 69 (6), 3359-9456 , 2019
    2019
    Citations: 108
  • Codon degeneracy and amino acid abundance influence the measures of codon usage bias: improved Nc ( N̂ c ) and ENCprime ( N̂ ′ c ) measures
    SS Satapathy, AK Sahoo, SK Ray, TC Ghosh
    Genes to Cells 22 (3), 277-283 , 2017
    2017
    Citations: 36
  • Wi-fi sensing based real-time activity detection in smart home environment
    AK Sahoo, V Kompally, SK Udgata
    2023 IEEE Applied Sensing Conference (APSCON), 1-3 , 2023
    2023
    Citations: 22
  • Wi-fi signal-based through-wall sensing for human presence and fall detection using esp32 module
    S Ajit Kumar, K Akhil, SK Udgata
    Intelligent Systems: Proceedings of ICMIB 2021, 459-470 , 2022
    2022
    Citations: 20
  • Material classification based on non-contact ultrasonic echo signal using deep learning approach
    AK Sahoo, SK Udgata
    Procedia Computer Science 235, 606-616 , 2024
    2024
    Citations: 13
  • Codon usage bias is not significantly different between the high and the low expression genes in human
    SS Satapathy, SK Ray, AK Sahoo, T Begum, TC Ghosh
    Int. J. Mol. Genet. Gene Ther 1 (1) , 2015
    2015
    Citations: 8
  • A novel fuzzy inspired machine learning framework for relative humidity estimation using time-of-flight of ultrasonic sensor
    AK Sahoo, SK Udgata
    Measurement 195, 111035 , 2022
    2022
    Citations: 5
  • Caption generation for sensing-based activity using attention-based learning models
    B Pati, AK Sahoo, SK Udgata
    IEEE Sensors Letters 8 (3), 1-4 , 2023
    2023
    Citations: 3
  • Wi-safehome: Wifi sensing based suspicious activity detection for safe home environment
    G Gorrepati, AK Sahoo, SK Udgata
    International Conference on Intelligent Human Computer Interaction, 291-302 , 2023
    2023
    Citations: 3
  • WiFi Sensing Model for Intrusion Detection in Smart Home Environment
    Gayathri Gorrepati, Ajit Kumar Sahoo and Siba K. Udgata
    2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), 329-334 , 2023
    2023
    Citations: 3
  • Machine Learning-Based Ambient Temperature Estimation Using Ultrasonic Sensor
    AK Sahoo, SK Udgata
    Next Generation of Internet of Things: Proceedings of ICNGIoT 2021, 657-668 , 2021
    2021
    Citations: 1
  • Novel Counterfactual Recourse Generation Using Causality-Guided Feature Perturbation
    K Sameer, J Swain, AK Sahoo
    International Conference on Computing, Communication and Learning, 459-471 , 2025
    2025