Siliguri Institute of Technology (informally SIT) is a private engineering and management college, established in 1999 in Siliguri, West Bengal, India. Initially the college was under North Bengal University and from the year 2004 came under the control of the newly formed West Bengal University of Technology (now renamed as Maulana Abul Kalam Azad University of Technology). The college is a part of Techno India Group.
Human behavior analysis significantly depends on facial expression recognition, where deep learning has enabled the development of visionary models that can surpass human-level performance. Explainable Artificial Intelligence (XAI) techniques are employed to validate the trustworthiness of a trained convolutional neural network by providing interpretable heatmaps, generated using recent techniques, including GradCAM, GradCAM++, LayerCAM, and ScoreCAM. These saliency heatmaps highlight the critical facial regions used by the classifiers, thus aligning the system’s behavior with human cognitive processes. Metrics such as average drop, confidence increase, and win percentage are utilized to assess the system’s reliability by analyzing these heatmaps, but they can not quantify the measure of trustworthiness. This study introduces Thresholding-based Evaluation Metrics in terms of Precision, Recall, and F-measure that not only assess the system’s reliability but also quantify the measure of trustworthiness of an XAI technique for a given classifier. Experiments are conducted on three benchmark datasets: CK+, RAFD, and RAF-DB, using classifiers including VGG19, ResNet18, GoogleNet, DenseNet121, and EfficientNet, and evaluated for different XAI techniques. The results demonstrate that the proposed metrics are as efficient as the traditional metrics and advance the assessment by quantifying the reliability measure, increasing their acceptability for human-centered applications. The source code will be made publicly available at https://github.com/Sayankumar007/FER-XAI-ThreshEvalMetrics .
Urban areas are expanding as the population grows uninterruptedly. Developing urban areas with proper planning and initiatives provides sustainable and healthy accommodation for the inhabitants. Proper policies and strategies are required to drive urban development. Proper initiatives need to be taken for developing an adequate living environment. Emerging technologies are playing leading roles in urban development. Artificial intelligence (AI) is able to make changes and develop automated decision-making solutions in every domain. The addition of AI provides robust solutions for urbanization. Smart city is the ultimate solution for progressive and advanced urban development. A healthy environment offers a healthy place for living. As the population in a smart city is growing continuously, a smart city offers a better, sustainable environment for an excellent living ambience. This study gives views about AI and its impact on sustainability concerns in the context of smart cities and urban development. Many issues regarding sustainability, in addition to environmental impact on urban development, are explored in this study.
In the current wireless era, where advanced wireless services (AWS-2) like the Internet of Things (IoT) with handheld devices are a major focus, the exploration and low-cost design of printed planar antennas is crucial. Circular sector microstrip antennas (CSMA) are ideal in this context, offering better performance than the conventional circular microstrip antenna (CCMA) despite their smaller size. However, selecting optimal parameters for CSMA remains a challenge due to the lack of established theory and design guidelines. This study presents a thorough, methodical analysis and outlines comprehensive design guidelines, aiding future designers in determining optimal configurations for specific GSM-IoT applications. Unlike earlier intuition-driven, simulation-based designs, this study presents the final CSMA design through quantitative analysis and compact design guidelines. Among various sector angles, the 170 degrees CSMA stands out, offering better performance than the classical circular microstrip antenna despite a 55% reduction in aperture area. This paper proposes the 170 degrees CSMA as a superior alternative to CCMA, enabling miniaturization with significantly improved polarization purity and good gain. The fabricated prototype was experimentally verified and compared to CCMA. The proposed CSMA achieves better gain, efficiency, and polarization purity compared to CCMA at the same operating frequency (1.87 GHz for GSM IoT applications).
In modern computational environments, safeguarding sensitive data and preventing unauthorized access require robust and efficient authentication mechanisms. This paper presents a secure authentication protocol that addresses two key objectives: validating user legitimacy prior to network access and maintaining data integrity and confidentiality during transmission. The proposed scheme leverages lightweight cryptographic techniques including hashing, encryption, and dynamic pseudonyms to ensure mutual authentication and secure session key exchange, while preserving user anonymity and preventing identity tracking. The protocol includes a secure initialization phase, user registration, and a Mutual Authentication and Key Exchange (MAKE) process, effectively countering common threats such as replay attacks, impersonation, and man-in-the-middle attacks. Designed for low-power and resource-constrained environments, the protocol minimizes computational overhead without compromising security. Comparative analysis with existing schemes demonstrates that the proposed method achieves a strong balance between security, efficiency, and scalability, making it highly suitable for modern distributed network architectures.
To find the most influential genes which are responsible for Glioblastoma (GBM) the differentially expressed tumorous cells and normal brain cells are explored using Protein–Protein Interaction network, Gene Oncology analysis and pathway analysis. Glioblastoma (GBM) (Kalinina J, Peng J, Ritchie JC, Erwin G, Meir V (2011) Proteomics of gliomas: Initial biomarker discovery and evolution of technology: Neuro Oncology 13(9):926–942. 10.1093/neuonc/nor078) is a very common rapidly-growing and aggressive type of brain tumor which is also classified as a grade IV astrocytoma by WHO, the uppermost grade. It is very crucial to understand the main molecular mechanisms those involve in GBM progression for emergent better diagnostic and treatment approaches because of its very poor post diagnosis survival rate. To completely understand the disease and extend targeted therapies recognition of main genes engaged in GBM pathogenesis is equally necessary. In our study two pair of gene chips GSE108474 and GSE50161as well as GSE12657 and GSE42656, which are collected from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) are analyzed and recognized to compare gene expression between Glioblastoma and normal samples and significantly common up regulated and down regulated genes are identified from each pair of chips. The PPI networks are investigated using the STRING 11.5 where the hub genes are identified from each pair using Cytoscape 3.9.1. Using GO enrichment analysis and KEGG pathway analysis on selected hub genes, the potential and significant genes are identified.
