Faculty Publications

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Scholarly Publications by Integral Academia

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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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    Securing the weakest link - compressive approaches to social engineering attacks prevention: Strategies for Enhancing Organizational Resilience Against Social Engineering Threats
    (IGI Global, 2023) Farooq Ahmad, Bably Dolly, Zohaib Khan
    In today's world, where digital interactions and online transactions are crucial for both personal and professional activities, social engineering attacks have become more common and sophisticated, threatening data security and organizational integrity. These attacks exploit a lack of awareness to gain illegal access to systems. While research includes various studies and methods for preventing these attacks, a thorough analysis of effective strategies is lacking. Current methods involve health campaigns, frameworks with human security sensors, user-focused approaches, and vulnerability models. This chapter thoroughly examines different types of social engineering attacks, such as phishing, vishing, and pretexting, highlighting the human element as the weakest link in cybersecurity. It then delves into prevention methods, stressing the need for awareness training and behavioral changes among users as a key defense. This chapter seeks to equip readers with practical insights and tools to protect against social engineering attacks by integrating current research and best practices