HGGA-TCG Model for Optimized Test Case Generation from UML Diagrams Jyoti Gautam Tiwari, Ugrasen Suman 2025 IEEE 5th International Conference on ICT in Business Industry and Government Ictbig 2025, 2025 Software testing is one of the key processes to guarantee the quality and reliability of a software system, especially in complex workflows represented by activity diagrams and state diagrams. Classical test case design usually based on manually selection, which is subject to incompleteness and redundancy, and incurs more cost and effort. Automatic test case generation through systematic modeling has been suggested as a way to deal with these problems. The key issue is to manage the path explosion problem in activity diagrams with parallel branches and loops and state diagrams with many transitions. However, although these methods can guarantee coverage of edge and transition and fullpath enumeration, they often result in redundant or unrealistic test cases which may lead to the overloading of testers during execution. To mitigate this, HGGA-TCG model uses an approach of the hybrid graph based test case generation technique and a GA based optimization is suggested. In this direction, the graph technique guarantees that all the pairs of edges and transitions will be covered, and the averaged coding approach eliminates redundancy by applying an optimal set of test cases. Experimental application to the hospital management workflow proved that GA reduces the number of test suite size and keep 100% coverage. This integration offers a good compromise between closeness and efficiency, working well for scalable and affordable measurement in the academic and industrial fields.
Towards a Comparative Analysis of Security Attacks in Cloud Data Migration Anjali Dhaman, Ugrasen Suman 2025 World Skills Conference on Universal Data Analytics and Sciences Worldsuas 2025, 2025 Cloud computing has revolutionized IT infrastructure by providing on-demand, scalable, and cost-efficient solutions. However, this advancement also leads to several security vulnerabilities in the cloud. This study categorizes the three cloud security attacks, i.e., cryptanalysis attacks, data storage attacks, and common cloud attacks. Each category is analyzed based on attack origin, targeted cloud layers, detection visibility, attack duration, preventive mechanism, and cloud service layer affected. The comparative analysis highlights the diverse impact of these cloud attacks, which can help to design a framework to protect data in the cloud environment.
Gait cycle segmentation and fall detection of healthy individuals using IMU sensors and deep learning Neha Gaud, Maya Rathore, Ugrasen Suman Sensor Review, 2025 Purpose This paper aims to address two important aspects of human walking. First, detect the periodicity of gait, and second, detect the fall of a healthy individual. The fall is a common phenomenon in cluttered environments. Design/methodology/approach To address these two aspects, the data is collected through three-axis Inertial Measurement Unit (IMU) sensors for different walking patterns. The IMU-collected gait data are time series in nature, and gait is a cyclic process; hence, gait data consists of periodicity and repetitive patterns. It is very important to know the gait periodicity time of the elderly to know the risk of falling due to walking impairment. In this paper, an automatic gait cycle extraction technique is proposed based on the autocorrelation function of time series data. The assumption is that there is maximum energy (peak) in the acceleration signal during the initial foot contact of the heel strike, which is the point where one gait cycle finishes. Findings The mean stride duration was calculated for 15 subjects, which is 1.06 s. It is aligned with the bench-mark work reported for healthy individual gait cycle periods [0.98 s, 1.2 s]. The validation curve and graph are also presented. Another contribution is the detection of falls while performing different daily activities in the real world. For fall diagnostics, the IMU-based SiSfall data set is used, which includes two different health age groups: adults (18 years and above) and the elderly, for 14 different walking activities from different directions and magnitudes. Research limitations/implications To automate identification, a hybrid deep learning model based on convolutional neural networks and long short-term memory (CNN-LSTM) is used to predict fall categories. The proposed CNN-LSTM model shows superior performance, with an accuracy of 99.12%. This research will provide the confidence to elderly individuals to walk independently. Originality/value The novelty of the work is the extraction of the gait cycle and the design of a personalized fall computation model with fast inference time suitable for real-time applications. The gait cycle prediction based on autocorrelation is significantly improved. To automate identification, a hybrid deep learning model based on CNN-LSTM is used to predict fall categories. The proposed CNN-LSTM model shows superior performance, with an accuracy of 99.12%. This research will provide the confidence to elderly individuals to walk independently.
FIBiLS: Fall Detection of Healthy Elderly Using IMU Sensor and BiLSTM Model Neha Gaud, Maya Rathore, Ugrasen Suman, Vijay Bhaskar Semwal IEEE Sensors Journal, 2025 Fall recovery refers to an individual’s ability to recover from external perturbation, and it is an obvious phenomenon in cluttered environments. Fall detection systems have evolved primarily for elderly individuals, who are more prone to falls that may lead to permanent disability or even death. In this paper, a novel deep learning-based approach for early fall detection & monitoring individuals is proposed, while they perform daily tasks. The current Industry 4.0 revolution has witnessed the growing popularity of IoT-based solutions, with wearable sensing technology driving the use of wearable sensors for detecting such activities. This research utilizes dual wearable inertial measurement unit (IMU) sensors, worn on the body, to monitor elderly individuals and detect potential falls during daily activities. The Sisfall dataset [1], a publicly available dataset of falls, is used to train the deep learning-based model. The dataset includes 14 distinct fall categories, which account for various directions and magnitudes of falls. Data is collected for experimental purposes to evaluate the fall detection capabilities of elderly individuals. The Sisfall dataset includes two age groups: Adults (18 years and older) and the Elderly (60 years and older). The research utilizes a 1-D Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) model to predict fall categories. The proposed FIBiLS (Fall detecting using IMU sensor data and BiLSTM model) deep learning model demonstrates superior performance, achieving an accuracy of 99.68% with fast inference time. To facilitate edge computing, the method is implemented on a Node MCU microcontroller board for fall detection. This approach outperforms previous research in both accuracy and complexity, providing better results with a more compact and less complex solution. This solution provides confidence to elderly individuals, enabling them to walk safely and independently.
Enhancing Security to Prevent Vulnerabilities in Web Applications Shekhar Disawal, Ugrasen Suman International Journal of Engineering Trends and Technology, 2024 The security of web applications remains a critical concern amidst escalating cyber threats and vulnerabilities. This research paper presents findings from an experimental study conducted on five websites using the pentest scanning tool. The experiment aimed to assess the vulnerabilities present in these web applications and identify potential security gaps. The prevalence of vulnerabilities such as SQL injection, Missing HttpOnly flag, and inadequate Content-Security-Policy underscores the urgent need for proactive measures to enhance web application security. Leveraging insights gained from the experiment, a novel Quality Enhancement Model for Secured Web Applications (QEMSWA) is proposed. This model integrates best practices and proactive strategies to fortify the security posture of web applications, addressing key areas such as the identification of assets, secure coding practices, code review, and effective vulnerability analysis. By proposing a recommendation model, this research seeks to empower organizations to mitigate risks and safeguard their web applications against emerging threats. Through the development of the QEMSWA model, this study contributes to ongoing efforts to establish a more resilient and secure digital environment.
Iterative Feature Elimination Method Using Artificial Neural Network for Software Effort Estimation Pranay Tandon, Ugrasen Suman International Journal of Engineering Trends and Technology, 2024 Effort estimation is one of the critical tasks for any software development team because estimation is the key to planning the software development life cycle activities with proper timeline and cost. On-time and quality delivery is most important to build customer trust and certainty. There are many features to be considered while estimating the efforts, but removing the weak features and finding the set of the strongest features for any estimation process is difficult. Deep learning is the most popular prediction technique for effort estimation because of its capacity to adapt and be accurate on different types of datasets. Artificial Neural Network is best suited to deep learning techniques for predicting effort, per industrial research. In this paper, a novel model based on artificial neural networks and an iterative feature elimination-based method has been proposed to estimate the efforts. With ranking features, the proposed method can find the optimized set of features to be used in the model and final efforts. COCOMO NASA 2 dataset is used to find the results.
ALBMAD: A Mobile App Development Approach International Journal of Intelligent Systems and Applications in Engineering, 2024
