Research Articles/ Conferences

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

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Start date: 2018Has files: Yes

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    Teachers’ Attitude Towards the Use of Digital Tools in Online Classes: A Theoretical Perspective
    (International Journal of Trend in Scientific Research and Development (IJTSRD), 2026) Shaista Ateeque
    The integration of digital tools into education has significantly transformed teaching-learning processes, particularly in the context of online classes. While technological advancements have expanded access, flexibility, and innovation in pedagogy, the effectiveness of online education largely depends on teachers, whose attitudes play a decisive role in shaping instructional practices (Ertmer & Ottenbreit- Leftwich, 2010). This theoretical paper critically examines teachers’ attitudes towards digital tools through psychological, pedagogical, and technological perspectives. Drawing upon established frameworks such as the Technology Acceptance Model (Davis, 1989), the Theory of Planned Behavior (Ajzen, 1991), the TPACK framework (Mishra & Koehler, 2006), and Constructivist Learning Theory (Piaget, 1972), the paper explores the multidimensional nature of teachers’ attitudes and their determinants. It further analyzes key influencing factors, pedagogical implications, and persistent challenges such as technostress, digital inequality, and contextual constraints (Tarafdar et al., 2015; UNESCO, 2021). The paper argues for a holistic, dynamic, and context-sensitive understanding of teachers’ attitudes and emphasizes the need for sustained institutional support, continuous professional development, and policy interventions to ensure meaningful and sustainable integration of digital tools in education.
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    3D Improve Zero Memory Set Partitioned Embedded BloCK Coding Algorithm for Hyperspectral Image
    (The Institute of Electrical and Electronics Engineers, Inc., 2026) Purushottam Lal Nagar, Shrish Bajpai, Naimur Rahman Kidwai
    Hyperspectral imaging maintain a crucial role for the remote sensing technologies. But, the amount of image data produce by the image sensors is huge, thus handling of this image data become an issue with the sensor and it’s performance. A compression algorithm is required to save the sensor memory, reduce data complexity and improves sensor performance. In past, many compression algorithms had been proposed but transform based compression algorithms performed better than other type of compression algorithms such as embeddedness, high coding efficiency and low coding complexity. Wavelet transform based compression algorithm has low coding memory and 3D Zero Memory Set Partitioned Embedded bloCK (3D-ZM-SPECK) achieve zero coding memory but has slightly low coding efficiency. To obtain higher coding efficiency, present compression algorithm is an advance version of 3D-ZM-SPECK which exploits spectral redundancy to achieve the high coding efficiency. It has been noticed from the simulation results on two different images that present compression algorithm high coding efficiency (∼4% to 5%) and zero coding memory requirement.
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    Mathematical Transform Based Set Partition Hyperspectral Image Compression Algorithms : A Comparative Review
    (The Institute of Electrical and Electronics Engineers, Inc., 2026) Osama Saleem, Shrish Bajpai, Divya Sharma, Naimur Rahman Kidwai
    Hyperspectral imaging has been extensively investigated as an emerging, promising technique for multiple domains. Originally derived from remote sensing, this technology now incorporates both machine vision and point spectroscopy within its scope of use. The end result is the transfer of hyperspectral images in large quantities from sensors to analysis centers and then, finally, to data centers using this method. Compression algorithms are used to ensure that these large-sized hyperspectral images are stored in a manner that is both efficient and secure. Compression algorithms using mathematical transforms works for lossy and lossless environment, which make it proper choice for the image sensors. Set partitioned based compression algorithms is a sub category of mathematical transform based algorithms which utilized the property of wavelet transform set structure to achieve the compression of image. This survey focuses on different hyperspectral image compression algorithms which utilized mathematical transform to achieve the compression. Additionally, we evaluate the most effective algorithms in terms of their coding efficiency and provide a detailed analysis of the primary factors that influence compression performance. Also, coding memory and embeddedness is also covered in the comparative analysis.
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    Understanding Science Teachers’ Epistemic Beliefs and Their Pedagogical Practices
    (Sciencedomain International, 2026) Manisha Singha, Waseem Zahra
    Teaching depends on two main pillars: the teacher's epistemic beliefs and their pedagogical practices. The first pillar adds to the theoretical framework, and the second pillar focuses on the practical aspect. The objectives of the present study are to analyse the relationship between science teachers’ epistemic beliefs and their pedagogical practices; to identify specific areas of alignment and misalignment between teachers’ epistemic beliefs and their teaching practices in science, and to explore how incomplete or inaccurate epistemic beliefs about the nature of science influence instructional decisions and teaching strategies. The study is qualitative in nature. The sample size is 50 in-service science teachers teaching classes from 6th to 12th grade, selected through purposive sampling. The interview findings revealed a nuanced relationship between science teachers’ epistemic beliefs and their pedagogical practices, showing that while many have sophisticated views, a significant "theory-practice disparity" exists. Notable misalignments occur when underdeveloped beliefs regarding the nature of science lead to passive, teacher-centred instruction rather than constructivist methods. These findings suggest that contextual barriers, such as rigid curricula and limited resources, often impede the operationalisation of epistemic beliefs, highlighting the need for targeted interventions to align theoretical understanding with classroom enactment.
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    Sustainability in Practice: Understanding the Intention-Behavior Gap Among Middle-Class Consumers in Lucknow
    (Széchenyi István University, Győr, Hungary, 2025) Firoz Husain, Rizwana Atiq
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    Supply Chain Transparency as a Marketing Differentiator in Healthcare: Advacing Responsible Consumption and Production under SDG 12
    (Széchenyi István University, Győr, Hungary, 2025) Rizwana Atiq , Shahab Ud Din
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    Pen, Pixel, or Paraphrase? Investigating the Relationship between Note-taking Modalities and Graduate Learning Outcomes
    (Sciencedomain International, 2026-05-07) Manisha Singh, Waseem Zahra
    The paper addresses the relationship between note-taking modalities and learning outcomes among graduate students and specifically examines how this relationship is mediated by cognitive processing. The study analyses the connection between different styles of note- taking, including one's own word summarising, structured/systematic, analogue, digital, and verbatim note-taking, and academic performance, based on the cognitive load theory and the generative learning model. A structured survey was used to gather data from 88 graduate students, and the data were analysed using Spearman's rank-order correlation since they were not normally distributed. The findings show that note-taking in one's own words has the strongest positive association with achievement, followed by structured note-taking, with verbatim transcription showing the weakest association. The digital and analogue modalities demonstrate the same level of success, which means that the medium does not have as significant an influence as cognitive engagement. The mindfulness of students in relation to note-taking as an active, metacognitive process that helps to understand, organise, and revise is also evidenced by qualitative data. The research is an addition to the existing literature that has placed cognitive processing as one of the key processes linking the activities of note taking with the learning outcomes, particularly in graduate learning which is cognitively demanding. The conclusions reflect the need to have pedagogical tools that promote active change and systematization of information.
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    Balancing Faith and Learning : A Systematic Review on Faith-Based Curriculum in Catholic Schools
    (TPM(Testing, Psychometrics, Methodology in Applied Psychology), 2025)
    Education in Catholic Schools uses faith-based approaches to nurture students morally, intellectually, and ethically for their holistic growth. Catholic Education aims at nurturing the student from all backgrounds, combining faith with schooling. Such an approach also tries to deal with opposing secularization, lack of trained staff, and teacher training constraints alongside dealing with contemporary education challenges.Teaching from a faith-based educational approach is examined and so are the challenges involved through the learner focusing on the moral and character development, teacher perception, and student perception in the Catholic School context. This review literature is comprehensive and employs the PRISMA framework to identify relevant studies focused on the outcomes of a faith-based approach to education in Catholic schools. We searched between 2004-2024 across multiple databases, including J Gate, JSTOR, Science Direct,ScopusProQuest. Initially, 968 studies were obtained, and after screening titles/abstracts, applying eligibility criteria, and assessing full texts, 26studies were selected for detailed analysis.
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    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.
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    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.