Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Subject: search.filters.subject.Deep learning

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Now showing 1 - 10 of 12
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    A review on WSN based resource constrained smart IoT systems
    (Springer, 2025) Haribabu, K.
    In Wireless Sensor Network (i.e. WSN) based resource constrained Internet of Things (i.e. IoT) environments, efficient data forwarding is achieved through cluster based mechanisms, where cluster heads facilitate communication among themselves and with the sink node. Data collected by each cluster head is temporarily buffered before being transmitted to the sink via multi-hop communication. The integration of advanced wireless technologies, such as 5th Generation (i.e. 5G) networks, offers significant benefits, including reduced latency, extensive coverage, improved spectral efficiency, and higher data transmission rates. Incorporating Device-to-Device (i.e. D2D) communication further enhances energy efficiency and offloads data traffic, addressing critical IoT requirements such as low latency, increased network capacity, and improved spectral and energy efficiency. Software Defined Networking (i.e. SDN) addresses diverse IoT network needs across domains like smart grids, healthcare, traffic signaling, agriculture, and smart homes by enabling efficient communication, network management, and innovative control procedures. However, SDN’s application for anomaly detection and primary defense against security threats in IoT systems remains underexplored. This research investigates the potential of the design of an intelligent mechanism for energy efficient, privacy preserving, and secure communication in WSN based resource constrained IoT systems. The proposed approach leverages advanced technologies such as SDN, Machine Learning (i.e. ML), Deep Learning (i.e. DL), D2D communication, Computer Vision, and Network Function Virtualization (i.e. NFV). Additionally, it emphasizes assessing and offloading specific IoT application functions onto the network’s edge to enhance performance. Moreover, the development of lightweight security mechanisms for secure communication in resource constrained IoT environments is also identified as a crucial research domain.
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    Women sport actions dataset for visual classification using small-scale training data
    (Sage, 2025-07) Bera, Asish
    Sports action classification representing complex body postures and player-object interactions, is an emerging area in image-based sports analysis. Some works have contributed to automated sports action recognition using machine learning techniques over the past decades. However, sufficient image datasets representing women’s sports actions with enough intra- and inter-class variations are not available to the researchers. To overcome this limitation, this work presents a new dataset named WomenSports for women’s sports classification using small-scale training data. This dataset includes a variety of sports activities, covering wide variations in movements, environments, and interactions among players. In addition, this study proposes a convolutional neural network (CNN) for deep feature extraction. A channel attention scheme upon local contextual regions is applied to refine and enhance feature representation. The experiments are carried out on three different sports datasets and one dance dataset for generalizing the proposed algorithm, and the performances on these datasets are noteworthy. The deep learning method achieves 89.15% top-1 classification accuracy using ResNet-50 on the proposed WomenSports dataset, which is publicly available for research at Mendeley Data.
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    Bayesian deep learning meets self-attention: a risk-aware approach to advertisement optimization
    (IEEE, 2025-05) Bhatia, Ashutosh; Tiwari, Kamlesh
    In the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R2 of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.
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    Deep learning approaches for driver distraction detection using driver facing cameras: literature review and empirical study using cnn classifiers on a 100-driver image dataset
    (2025-05) Bhatia, Ashutosh; Sharma, Yashvardhan; Tiwari, Kamlesh
    Distracted driving contributes to thousands of fatalities and injuries globally. According to India’s Ministry of Road Transport and Highways (MoRTH), distraction-related behaviors such as rear-end and off-road collisions accounted for nearly one-fourth of all traffic incidents in 2022. The U.S. National Highway Traffic Safety Administration (NHTSA) reported 3,275 deaths and over 324,000 injuries from distraction-related crashes in 2023. In Europe, the European Road Safety Observatory (ERSO) observed handheld phone use by drivers in up to 9.4% of vehicles across member states, with self-reported texting rates reaching 53%. Despite numerous studies and surveys on driver distraction detection, existing literature remains fragmented, often combining multiple sensor modalities or distraction with related driver states such as fatigue. Prior empirical efforts also lack a unified benchmarking strategy to assess model generalization under shifts in viewpoint or spectral input. This paper presents a focused survey and empirical study of visiononly distraction detection using deep learning models applied to driver-facing camera inputs. It introduces a conceptual model linking behavioral cues to cognitive distraction, defines the visionbased Driver Distraction Detection (vDDD) system with alert logic, and develops structured taxonomies of datasets, architectures, and learning strategies. Using the 100-Driver dataset, the empirical study evaluates 26 CNN classifiers under 64 crossdomain configurations, systematically analyzing generalization across modality and camera view changes. Results show that frontal RGB-trained models generalize better than their NIRtrained counterparts and that lightweight models trade off accuracy under rare class scenarios for faster inference. The study establishes the vDDD paradigm as a vision-based behavioral modeling approach for distraction detection using driver-facing camera data. It outlines future research directions in spectrumaligned augmentation, attention modeling, and lightweight visuallanguage fusion, emphasizing deployment-focused strategies such as quantization, contrastive learning, and progressive fine-tuning.
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    Optimizing liquid neural networks: a comparative study of ltcs and cfcs
    (IEEE, 2024) Challa, Jagat Sesh
    Liquid Time Constant Networks (LTCs) and Closed Form Continuous Networks (CFCs) are recent time-continuous RNN models known for superior expressivity and efficiency in time-series prediction and autonomous navigation. This paper provides an accessible overview of these models and investigates their performance on tasks like Atari ’Breakout’ behavior cloning, steering angle prediction, and Global Horizontal Irradiance (GHI) forecasting. We optimize LTC and CFC cells within network structures, comparing them with LSTM. Detailed experiments highlight the impact of various hyperparameters, underscoring the effectiveness of LTCs and CFCs in dynamic prediction tasks.
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    Fusion of multivariate time series meteorological and static soil data for multistage crop yield prediction using multi-head self attention network
    (Elsevier, 2023-09) Goyal, Poonam; Goyal, Navneet
    Yield prediction is helpful for timely harvest management, crop planning, and food security. It depends on many factors like location, climate, soil characteristics, genotype, etc. The data used in yield prediction is a typical mix of highly dynamic time series (meteorological) and static (soil) data. We effectively integrate the two data categories to train a deep-learning model. We introduce a novel attribute selection algorithm to select the most discriminating soil features and modified it for depth-level selection which suggests the most appropriate depth of soil factors for a given crop. We have also introduced a novel approach for modeling the problem where spatiality is handled by clustering locations based on their meteorological and soil characteristics which allow our model to learn spatial patterns. The variation in sowing and harvesting time across locations is taken care of by using padded crop cycle data. We have also taken several other design decisions and validated them on existing models. We experimented with NC94 data of the US with three major crops soybean, wheat, and corn, and predicted yield at the county-level. We have also modified our model to perform in-season and multi-time horizon prediction. The results of our proposed YieldPredictNet show that it outperforms competing techniques.
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    User Profiling Using Smartphone Network Traffic Analysis
    (IEEE, 2021) Bhatia, Ashutosh; Tiwari, Kamlesh
    The recent decade has witnessed phenomenal growth in communication technology. Development of user friendly software platforms, such as Facebook, WhatsApp etc. have facilitated ease of communication and thereby people have started freely sharing messages and multimedia over the Internet. Further, there is a shift in trends with services being accessed from smartphones over personal computers. To protect the security and privacy of the smartphone users, most of the applications use encryption that encapsulates communications over the Internet. However, research has shown that the statistical information present in a traffic can be used to identify the application, and further, the activity performed by the user inside that application. In this paper, we extend the scope of analysis by proposing a learning framework to leverage application and activity data to profile smartphone users in terms of their gender, profession age group etc. This will greatly help the authoritative agencies to conduct their investigations related to national security and other purposes
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    SmartDriveAuth: Enhancing Vehicle Security with Continuous Driver Authentication via Wearable PPG Sensors and Deep Learning
    (Springer, 2024-04) Bhatia, Ashutosh; Tiwari, Kamlesh
    The paper introduces a novel approach for continuous driver authentication in vehicle security, utilizing wearable photoplethysmography (PPG) sensors and Long Short-Term Memory (LSTM)–based deep learning. This study aims to overcome the limitations of traditional one-time authentication (OTA) methods, which typically involve passwords, PINs, or physical keys. While effective for initial identity verification, these conventional methods do not continuously validate the driver’s identity during vehicle operation. The proposed system leverages an LSTM-based prediction model to efficiently predict the subsequent PPG values using the raw PPG signals from wrist-worn devices. The predicted values are continuously compared with actual real-time data (received from the sensors) for authentication. The proposed system eliminates the need to permanently store user biometrics in a database. Motion artifacts and momentary disruptions have minimal impact on system performance. Experimental validation was conducted with 15 participants driving in varied conditions to simulate real-life driving conditions. The study evaluated the system’s accuracy, achieving an Equal Error Rate (EER) of 4.8%, demonstrating its potential as a viable solution for continuous driver authentication in dynamic environments.
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    Enhancing security through continuous biometric authentication using wearable sensors
    (Elsevier, 2024) Bhatia, Ashutosh; Tiwari, Kamlesh
    The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.
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    ConvXSS: A deep learning-based smart ICT framework against code injection attacks for HTML5 web applications in sustainable smart city infrastructure
    (Elsevier, 2022-05) Dua, Amit; Gupta, Shashank
    In this paper we propose ConvXSS, a novel deep learning approach for the detection of XSS and code injection attacks, followed by context-based sanitization of the malicious code if the model detects any malicious code in the application. Firstly, we briefly discuss XSS and code injection attacks that might pose threat to sustainable smart cities. Along with this, we discuss various approaches proposed previously for the detection and alleviation of these attacks followed by their respective limitations. Then we propose our deep learning model adopting whose novelty is based on the approach followed for Data Pre-Processing. Then we finally propose Context-based Sanitization to replace the malicious part of the code with sanitized code. Numerical experiments conducted on various datasets have shown various results out of which the best model has an accuracy of 99.42%, a precision of 99.81% and a recall of 99.35%. When compared with other state of the art techniques in this domain, our approach shows at par or in the best case, better results in terms of detection speed and accuracy of CSS attacks.