Research Articles/ Conferences

Permanent URI for this collectionhttp://192.168.24.11:4000/handle/123456789/58

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Impact of Institutional Repositories’ on Scholarly Practices of Scientists
(Library Philosophy and Practice University of Nebraska, 2018-02-01) Shukla, Prachi; Ahmad, Naved
Novel drug design and bioinformatics: an introduction
(De Gruyter, 2021) Mohammad Kalim Ahmad Khan ,Salman Akhtar
In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.
Security Test Case Prioritization through Ant Colony Optimization Algorithm
(Tech Science Press, 2023) Mohd Faizan; Mohd Waris Khan; Abdulaziz Attaallah, Khalil al-Sulbi, Areej Alasiry, Mehrez Marzougui, Alka Agrawal, Dhirendra Pandey
Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection (APFD) metric, comparing it with traditional techniques. It has been applied to a Mobile Payment Wallet application to validate the proposed approach. The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric. The ACO-based technique achieves an APFD of approximately 76%, two percent higher than the second-best optimal ordering technique. These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases, saving time and improving software security overall.
BEYOND BARRIERS: A TAPESTRY OF PEDAGOGICAL STRATEGIES FOR INCLUSIVITY AND SUSTAINABLE PROGRESS
(International Journal of Emerging Knowledge Studies, 2024) Dhriti Tiwari
In an ever-evolving landscape of education, the imperative to foster inclusivity and sustainable progress stands as a foundational challenge for educators worldwide. The present age, marked by an unparalleled range of differences, educators are faced with the task of adapting their methodologies to accommodate the distinct requirements and inclinations of a diverse student population. Concurrently, the increasing worldwide demand for sustainable practices underscores the imperative to incorporate environmental consciousness and responsible civic engagement into the core of educational systems. This research article explores innovative educational approaches that go beyond traditional boundaries to foster comprehensive and long-lasting progress. It examines the integration of differentiated instruction, universal design for learning, and collaborative learning in a harmonious and inclusive learning environment. The study also explores a tapestry of pedagogical approaches for accommodating diverse learning needs and addressing 21st-century challenges. Cultural responsiveness, environmental education, and technological integration are key to sustainability and inclusivity. The research shows that these pedagogical practices can change education for the betterment of students and the world.
A study on perceptions and practices of STEAM-based education with university students
(Social Sciences & Humanities Open, 2024) Manisha Singh
The present survey-based study was conducted with (n = 317) university students in Lucknow city of India to examine their perceptions and practices about Science, Technology, Engineering, Arts, and Mathematics (STEAM) education. The data was collected from students belongingto different academic streams including Science, Economics, Arts, Engineering, Architecture, Computer Sciences, and Business and Management etc. from different higher educational institutions of the city. The snowball sampling method was adopted for the study. A tool in the form of a 5-pointer rating scale with three categories viz. Awareness, Perception and Implementation along with two dimensions in the Awareness category, four dimensions in the Perception category and one dimension in the Implementation category was constructed to collect data with a total of 23 items which were related to majorly three mentioned categories and demographic information. Frequency data analysis and the Kruskal-Wallis H test were used for analysis. The findings showed that most students hold a positive perspective towards the advantages of STEAM in their learning process. Still, despite the positive perspective, significant differences in the perception and implementation category of STEAM-based education was seen. The teaching-learning process is not planned as per STEAM education and classroom teaching lacks in in-cooperating STEAM. A significant difference was observed in the perception and implementation of STEAM-based education. The study showed that more than 70 per cent of the students believed that STEAM-based Education may enhance deeper understanding, constructivist approach, professional efficiency and critical thinking. The findings also highlighted the positive role of STEAM Education in infusing aesthetic elements and holistic approach in students.
Assessing the Impact of Human-Induced Disturbances on the potential Biomass and Carbon content in two wildlife sanctuaries of Uttar Pradesh, India
(EM International, 2024) Azram Tahoor, Azra Musavi, Jamal Ahmad Khan
This research paper presents a detailed analysis of biomass assessment of woody species in Katerniaghat and Kaimoor Wildlife Sanctuary situated in Bahraich and Mirzapur district of Uttar Pradesh. Through a reconnaissance survey, area was stratified into high, medium and low disturbed site based on the presence of human induced disturbance indicators. Circular plot method of 10m radius was used for vegetation assessment. Data on vegetation like woody species name, number of individuals, Girth at Breast’s height were recorded. Biomass and carbon stock of tree species was calculated from each stratified site of both sanctuaries. The finding of the study showed that highest biomass was estimated from high disturbed site of Katerniaghat. In Kaimoor, medium disturbed site showed maximum biomass. The present study aims to provide a comprehensive understanding of its carbon stock and sequestration potential. Biomass assessment is crucial for sustainable forest management and climate change mitigation strategies. Our findings reveals the negative impact of varying levels of anthropogenic disturbance on the forest biomass of both protected areas and help in better understanding of conservation and management and forests and carbon offset initiatives.
A Review on Detection of Species Extinction Risk Through Artificial Intelligence
(Vivekanandha Arts and Science College for Women, NH-47, Salem-Kovai express highway ,Veerachipalyam, Sankagiri - west(Po.),Sankagiri (Tk.), Salem (Dt.), Tamilnadu – 637 303, India, 2026) Mohammad Usama, Irfan Aziz
Species extinction is a major concern for sustenance of ecosystem and its conservation.Species are exposed to different levels of threats due to over exploitation, habitat loss, climate change, introduction of invasive species, genetic assimilation and pollution. These challenges ultimately lead to complete disappearance of species from the face of the Earth, known as the extinction of species. With the advent of artificial intelligence, interest in stimulated in assessment of risk to species. These risks or threats responsible for species extinction can now be detected by automated analysis of vast and complex ecological datasets. This helps in real time monitoring and tracking of endangered animals. Computer vision, machine learning, and acoustic sensors are employed in detection of species extinction risk. Artificial intelligence process camera trap footages, images and audios for tracking population of species, identification of threats such as poaching and prediction of habitat loss. AI algorithms are able to detect, in real time, hunting activities, forests fires and distress in animals., requiring immediate intervention. Machine learning models help in predicting species extinction risk by analyzing ecosystem interactions and helping in preventing extinction cascades. AI models are able to connect field data with satellite imagery for generating maps indicating likelihood of habitat availability for endangered species. Automated Wildlife Monitoring Platforms such as Conservation AI and Wildlife Insights are employing models such as SpeciesNet for analyzing millions of images and videos from drone and camera-trap footages. This automation can identify species with an accuracy of around 99%. AI tools such as Perch analyze soundscapes for identification of rare species and can monitor health of biodiversity through bird calls.
A Review on Major Phytochemicals Benefits for Environment & Human Health
(Vivekanandha Arts and Science College for Women, NH-47, Salem-Kovai express highway ,Veerachipalyam, Sankagiri - west(Po.),Sankagiri (Tk.), Salem (Dt.), Tamilnadu – 637 303, India, 2026) Irfan Aziz, Mohammad Usama
Phytochemicals are bioactive compounds present in plants. These include carotenoids, polyphenols, flavonoids, isoprenoids, polyphenols, and carotenoids. Scientific studies have established a relationship between the consumption of phytochemicals and its beneficial impact on human health. They play a major role as antioxidants, anti inflammatory agents, modulating metabolic functions and as immune boosters. They are helping in mitigating oxidative stress and prevention of diseases such as diabetes, obesity, cancer and cardiovascular problems. Nowadays, foods containing phytochemicals are also used as a constituent i.e. functional foods and the concentrated form of phytochemicals i.e. nutraceuticals are used as a preventive measure or cure for many diseases. The health benefits of these phytochemicals depend on their purity and structural stability. This is the reason for popularization of these phytochemicals in nutraceutical industry. Phytochemicals are also reported to be used in environment as an eco-friendly pest control, bioremediation, and for understanding plant adaptation to climate change.
Hybrid Particle Swarm and Gravitational Search Optimization for Intelligent Battery Health Estimation
(IEEE, 2026) M. S. Hossain Lipu, Md Ibrahim, Shaheer Ansari, Kashem Muttaqi, Danny Sutanto
This paper presents a novel hybrid optimization framework for data-driven estimation of the state of health (SoH) of lithium-ion batteries (LIBs). Existing data-driven SoH estimation methods struggle to select suitable model hyperparameters that consider capacity regeneration phenomena and to identify meaningful data samples from LIB parameters. The specific contribution to this research lies in the integrated optimization strategy, which bridges the limitations mentioned. The dataset has 31 features, comprising MIT-Stanford lithium-ion battery profiles. It employs an intelligent hybrid approach that combines the gravitational search algorithm (GSA) with particle swarm optimization (PSO) to fine-tune recurrent neural network (RNN) parameters, such as the hidden layer neurons and learning rate. The combined GSA and PSO algorithms integrated with RNN improve the SoH estimation accuracy through better search efficiency and faster convergence. The 31 LIB parameter samples are closely linked to capacity degradation and are well-suited to form the data framework. The proposed model demonstrates high accuracy in SoH estimation, particularly when applied to cell c33 from MIT-Stanford lithium-ion battery profiles. Results show that the RNN optimized with the GSA-PSO algorithm for the c33 dataset achieved a mean squared error (MSE) of 1.30×10−8, a root mean square error (RMSE) of approximately 0.0142, and a mean absolute percentage error (MAPE) of 0.0096.
Reinforcement Learning Controlled Variable Speed Limits in Urban Expressway Mixed Traffic
(IEEE, 2026) Alfayez Ahmad, Mohd Sadat, Syed Aqeel Ahmad, Shrish Bajpai, Mehmet Ali Silgu
Increase in Rapid urbanization has intensified traffic congestion, delays, and emissions on urban expressways, revealing the limitations of conventional control strategies. This study aims to develops a Reinforcement Learning (RL) based Variable Speed Limit (VSL) control framework using a Deep QNetwork (DQN) implemented in the Simulation of Urban Mobility (SUMO) environment to enhance traffic efficiency at an expressway merging section. Traffic data used was collected using a video camera recorder and radar speed gun, with vehicle trajectories extracted through the Traffic Data Extractor developed by IIT Bombay. The model implemented was calibrated and validated using field observations from two sitesone representing uninterrupted flow and the other in an onramp merging area. The simulation compared three configurations: a baseline case without control, a conventional rule-based VSL controller, and the proposed DQN-based VSL approach and the findings reveal that the DQN agent achieved a 18.8% reduction in total travel time compared to the baseline, while the rule-based VSL controller worsened performance by 21.7%. The learning-based controller effectively mitigated congestion, reduced shockwave formation, maintained higher average speeds, and improved travel time reliability under dynamic and stochastic traffic conditions demonstrating that a reinforcement learning-driven VSL system can significantly enhance both traffic flow efficiency and user-level reliability, outperforming traditional heuristic control strategies on urban expressways.
Contourlet Transform Based Listless Block Cube Tree Coding for Hyperspectral Images
(IEEE, 2026) Shrish Bajpai, Divya Sharma, Naimur Rahman Kidwai
The performance of compression algorithms at low bit rates is a critical benchmark, particularly for hyperspectral imaging where the fidelity of reconstruction is paramount. Although wavelet-based approaches are prevalent in the literature, they frequently present a trilemma of undesirable trade-offs, suffering from insufficient coding efficiency, exorbitant memory requirements, or high computational overhead. The proposed compression algorithm employees advance wavelet transform to leverage both spectral and spatial redundancies found in HS data cubes. Present study explores the utilization of block tree coding algorithm and contourlet transform to compress HS images. The primary goal is to enhance the coding efficiency while minimizing storage and transmission requirements. The proposed compression algorithm is evaluated on four benchmarks against eight state of art other compression algorithms on three performance metrics named coding efficiency, coding memory and coding complexity. In addition, it has low encoding/decoding time than other compression algorithm. From the simulation result, it has been clear that proposed compression algorithm has 2% to 4% increase in coding efficiency compared to the other state of art compression algorithms.
Simulation of Carbon Nanotube FETs for Power-Efficient Digital Circuits
(IEEE, 2026-01-23) Imran Ullah Khan, Somendra Shukla, Rani Kiran, Nupur Mittal, Mohd. Amir Ansari
This work addresses the limitations of silicon scaling by exploring Carbon Nanotube FETs (CNTFETs) as alternatives. Schottky Barrier CNTFETs (SBCNTFETs) suffer from ambipolar currents, which reduce the Ion/Ioff ratio; this can be improved through optimised design parameters. A Double Gate (DG) structure is modelled to enhance gate control, achieving better Ion/Ioff ratio (5.55×105) and subthreshold swing (87.3mV/ decade). A mathematical model for DG-SBCNTFET is developed and validated with Nano TCAD ViDES simulations. Using optimised parameters, a DG-SBCNTFET-based 6T SRAM cell is designed and simulated in HSPICE, demonstrating 20% lower power dissipation compared to a conventional CNTFET SRAM cell without compromising stability.
Performance Analysis of Double Material Cylindrical Surrounding Gate Mosfet
(IEEE, 2026-01-25) Nupur Mittal, Piyush Charan, Imran Ullah Khan, Zohaib Hasan Khan
The investigation explored how self-heating and temperature sensitivity impact the linearity of a double-material cylindrical surrounding gate MOSFET. Results indicate that self-heating can reduce the device's linearity by decreasing transconductance and increasing output conductance. Temperature sensitivity affects the device's mobility and threshold voltage. Hence, it is essential to count on these factors during the planning and assessment of double-material cylindrical surrounding gate MOSFETs (DMCSG MOSFET) to guarantee peak performance. The study utilized a TCAD 3-D device simulator to obtain these results. The DMCSG MOSFET is an innovative transistor design that demonstrates improved performance characteristics compared to standard MOSFETs. This device utilizes a double material cylindrical gate surrounding the channel to enhance control and improve electrostatic integrity, resulting in better transistor performance. With its cylindrical gate structure enclosing the channel, the DMCSG MOSFET achieves superior gate control while minimizing short channel effects. By incorporating two different materials in the gate, it enhances electrostatic integrity, reducing gate leakage and improving device reliability. This transistor design also presents several advantages over traditional planar MOSFETs. The cylindrical gate geometry enables better electrostatic control, resulting in reduced power consumption and improved switching speed. Moreover, the DMCSG MOSFET demonstrates outstanding scalability, rendering it fitting for cutting-edge integrated circuit designs. Within this abstract, we provide a synopsis of the DMCSG MOSFET's pivotal characteristics, encompassing its dualmaterial surrounding gate configuration and enhanced electrostatic management. We emphasize its benefits over traditional MOSFETs, such as diminished power consumption, heightened switching speed, and augmented scalability. The DMCSG MOSFET shows great potential for use in high-performance electronic devices and integrated circuits, paving the way for advancements in the field of semiconductor technology.