Government Engineering College Bikaner

This study addresses the urgent challenge of leveraging artificial intelligence (AI) to advance sustainable development during global crises, such as the COVID-19 pandemic, which amplified environmental, social, and economic challenges. The research problem focuses on understanding how AI can support the Sustainable Development Goals (SDGs), a set of essential global objectives established by the United Nations in 2015 to promote prosperity, social equity, and environmental protection by 2030. It also explores the applicability of the Environmental Kuznets Curve (EKC), a hypothesis suggesting that environmental degradation increases with early economic growth but declines after a certain income threshold, to AI-driven sustainability initiatives. Using a mixed-methods approach, including literature reviews, case studies, and quantitative analysis, the study investigates AI applications in health care, energy efficiency, Internet of Things (IoT)-enabled smart cities, industrial innovation, urban planning, and education. Key findings reveal that AI enhances energy optimization (e.g., 20

Software delivery in a timely planned manner has been of utmost priority for the software development organization. The new changes to the release schedule can cause further delays in the release causing frustration for end users. As a whole the goal of the software development is to keep the customer base happy but doing so gets difficult as there is no endpoint of introducing new changes in the release to meet the expectations of end users. This causes more expense in software development and delays the schedule of future releases in software development. Our approach is to introduce a generic template of release cadence using test early methodology to support the execution of web browser-based tasks framework. The approach consists of various factors i.e. Time, quality of developed software and timely release which ensures that the release has a hard commit date and at a certain point introducing new changes in the software risks the associated components of feature under development and AUT (application under test).

Additive manufacturing is a growing transformative technique for industrial fabrication of lighter and stronger components, which is also called as 3D printing. The Fused Deposition Modeling (FDM) based additive manufacturing processes is employed for manufacturing of polymer components for different applications. Printing time commonly known as build time is an important aspect of FDM based 3D printing. In current investigation rectangular blocks are printed in FDM with 13 different infill patterns. The analysis is made on the basis of time taken by different infill patterns for different shapes. Here total build time is further broken into retraction, outer wall printing, inner wall printing, skin printing, infill printing, and heating. It is observed from analysis that while printing rectangular shape, the printing time is increased by 37.50

Sentiment analysis, an essential task in natural language processing, has evolved rapidly with the emergence of deep learning architectures. Although numerous studies have applied models such as CNNs, RNNs, and Transformers to sentiment classification, a comprehensive comparative synthesis across these architectures remains limited. Existing reviews often focus on specific model families or narrow application domains, leaving a gap in understanding how different deep learning paradigms collectively influence sentiment analysis performance. This paper addresses this gap by presenting a systematic review and comparative analysis of deep learning-based sentiment analysis techniques published between 2020 and 2025. The study examines major architectures—CNN, RNN, attention-based, transformer, hybrid, and graph-based models—covering their design, datasets, performance metrics, and limitations. Furthermore, it proposes a unified taxonomy of sentiment analysis models and identifies open research challenges related to data imbalance, cross-domain adaptation, multilingual processing, and model interpretability. The review aims to provide researchers with an integrated perspective on current advancements while outlining promising directions for future sentiment analysis research.

Modern System-on-Chips (SoCs) incorporate a diverse array of on-chip instruments and proprietary data to support functions such as testing, diagnosis, post-silicon debugging, in-field monitoring, authentication, and counterfeit detection. To provide flexible access to these instruments, testing infrastructure such as IEEE Std. 1687 (IJTAG) is embedded into the ICs. However, such broad access can be exploited by adversaries to facilitate side-channel attacks. To address this concern, a complete security protocol has been recently proposed, which aims to protect IJTAG against both internal and external attacks by encrypting the Test Mode Select (TMS) signal, responsible for controlling the JTAG TAP controller, through specialized access software. In this work, a comprehensive security analysis of the IJTAG framework is presented. It is demonstrated that the proposed complete security protocol remains vulnerable to a chosen-plaintext attack initiated by an untrusted tester. The attacker can exploit the communication channel between the tester and the access software to compromise the system. Notably, even under the assumption of a high-security environment, the attack enables successful recovery of the templates used for TMS encryption in under 2 milliseconds. Additionally, we propose a session-bound obfuscation in which all TMS bits are embedded sequentially, yielding strong resistance to the proposed attack.