Indian Education Society's V. N. Sule Guruji English Medium School

The rapid growth in vehicular traffic has heightened the need for enhanced road safety and effective speed regulation. Speeding, particularly in sensitive areas like school zones and hospital vicinities, remains a major cause of accidents due to human error, low visibility, or disregard for signs. This paper presents the design and validation of an AI-assisted proof-of-concept system for real-time road sign detection and automatic speed control. The system integrates a Raspberry Pi 4B for image processing and an ESP32 microcontroller for speed regulation through Pulse Width Modulation (PWM). A high-resolution camera captures live video of the road ahead, where a Region-based Convolutional Neural Network (R-CNN) identifies potential traffic signs, and a Convolutional Neural Network (CNN) classifies them into specific categories such as school zone, hospital zone, or railway crossing. Upon detection, the system compares the vehicle’s current speed with the corresponding zone limit and automatically adjusts motor speed if overspeeding is detected, while a 16 × 2 LCD provides real-time feedback to the driver. Experimental testing under varied lighting and environmental conditions demonstrated promising detection accuracy, low-latency response, and effective speed regulation, confirming the feasibility of deploying deep learning models on low-cost embedded hardware. Rather than introducing new algorithms, this study focuses on the practical integration and performance evaluation of established CNN and R-CNN architectures within a resource-constrained environment. Future work will explore lightweight models and expanded datasets to further improve real-time performance and scalability in complex traffic scenarios.

By addressing the critical gaps in current practices, the objective of the review is to highlight emerging trends and provides insights into the optimization of drug solubility for improved pharmacological outcomes. The literature search was done and rlevent articles were collected from various database like Springer, Science Direct, Taylor and Francis, Wiley and pubmed. Poor solubility of active pharmaceutical ingredients (APIs) significantly hinders their bioavailability and therapeutic efficacy, presenting a critical challenge in drug formulation. This review provides a comprehensive exploration of traditional and advanced techniques for solubility enhancement. Established methods, including salt formation, particle size reduction, and pH adjustment, are compared with cutting-edge strategies such as nanotechnology, electrospun nanofibers, spray drying, and supercritical fluid technology. These innovative approaches leverage mechanisms like particle size reduction, amorphization, and nanostructure engineering to enhance dissolution rates, stability, and controlled release profiles. The article discusses the Biopharmaceutical Classification System (BCS) and its relevance in tailoring solubility enhancement strategies for poorly water-soluble compounds.

Ureido group is an interesting moiety as hydrogen donor and acceptor functionalities are present. Peptides are intriguing molecules as their properties are easily tuned and they are biocompatible. Therefore, four ureido dipeptide esters were synthesized using simple solution phase synthesis and characterized. Their biological activity was investigated against Streptococcus pyogenes, Staphylococcus aureus and E. coli. A rheological study was carried out to study the gelation properties of the synthesized compounds. Their ability to self-assemble into different morphologies was studied using SEM. The morphology of cubes, sheets, spheres, and rods was observed in the SEM study. These compounds will have potential applications in the medicinal field as drug delivery vehicles.

This article introduces a microwave sensor designed for defect detection, utilizing a novel split ring resonator, with the primary objective of identifying concealed cracks beneath the coating of carbon fiber reinforced polymer (CFRP) structures. The sensor operates by detecting variations in frequency caused by disturbances in the electric field due to surface cracks in the CFRP material. These frequency shifts are used to characterize the width, depth, and length of the cracks. Initially, a unit sensor is employed, which is based on a 50 transmission line coupled feed, to detect cracks in metal structures. This sensor can provide information about the width, length, and depth of a single crack at a time. Subsequently, an array sensor configuration is utilized, incorporating multiple split ring resonators to enable the detection of multiple cracks simultaneously. The sensor's functionality can be switched between a band-stop and a band-pass filter mode, depending on whether cracks are present or absent in the structure being examined. Operating within the frequency range of 2 to 3 GHz, this sensor system exhibits the remarkable capability to identify cracks with dimensions as small as 0.5 mm, encompassing width, length, or height, without regard to their orientation. This sensor system also extends its utility to the detection of large-area metal surface cracks, expanding its applicability to various scenarios, including the inspection of bridges, oil and gas pipelines, and more.

This paper uses the super efficiency SBM model based on unexpected output to calculate the industrial carbon emission efficiency of 30 provinces in China from 2005 to 2019, and uses spatial autocorrelation and other methods to conduct an empirical analysis on the dynamic evolution process and temporal and spatial differences of carbon emission efficiency. The results show that China's industrial carbon emission efficiency is still in the low efficiency stage, with an average value of 0.34. In the study period, although the overall trend is rising, the spatial and temporal differences between provinces are obvious. The overall Moran's I index is subject to the impact of social policy environment on the time path, showing an overall "M" type fluctuation trend, and its spatial difference characteristics change in stages. At the same time, there is a significant global spatial agglomeration effect across the country, and there is a spatial dependency between provinces. The agglomeration trend is significant, forming a cluster with the eastern coastal area as the high value center, and the northwest inland area as the low value center. The Getis OrdGI * index reveals that the local spatial agglomeration characteristics of China's industrial carbon emission efficiency always maintain the pattern of "hot in the east and cold in the west", which echoes the overall spatial evolution pattern of China. The hot spots and sub hot spots of China's industrial carbon emission efficiency are mainly concentrated in the southeast coastal areas with relatively superior economic development, and present a "semi enclosed" pattern. Cold spot area and sub cold spot area gradually evolved into a "suppression" pattern dominated by provinces in northwest China, comprehensively suppressing hot spots. This paper conducts an all-round evaluation of China's industrial carbon emission efficiency from the perspective of regional differentiation, and deeply studies the regional differences and spatial evolution pattern of carbon emission efficien cy in various provinces. It is crucial to achieve the goal of carbon emission reduction in China, and has important practical value and practical significance.

Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

Pattern classification is also called pattern reorganization. The classification of pattern is one of the critical problems in Artificial Intelligence. COVID-19 is the most viral lung disease that has put the whole world in such a difficult situation that it has become necessary to develop a machine-learning algorithm to classify lung disease. This research paper is ready to propose Artificial Intelligence Intelligence-based Lung Disease Classification by using an Evolutionary Deep Learning Paradigm to solve the said problem. It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form user-friendly platforms for physicians and researchers. The main objective of the proposed system is to find the probability, diagnosis, and treatment of the COVID-19 disease. Artificial Neural Network-based tool for challenges is associated with COVID-19. There is some specification of our platform as it includes various forms of input data containing medical as well as clinical data. This helps in improving the performance of the system. Experimental results are calculated and statistically analyzed. For benchmarking, the performance of the proposed approach is compared and statistically analyzed.

This paper aims to present a salary prediction system. This system use the economic liberalization of Indian markets in early 90s boosted the economic growth of the nation in various sectors over the next two decades. One such sector that has seen a massive growth in this time is Information Technology (IT). The IT industry has played a very crucial role in transforming India from a slow moving economy to one of the largest exporters of IT services. This growth created a huge demand in the labour markets for skilled labour, which in turn made engineering one of the top choices of study after high school over the years. In addition, the earning potential and an opportunity to contribute to technology advancements after engineering, makes it a popular choice of study. These growth dynamics along with the diversified education and labor markets demands gives insight into the factors affecting the employment outcomes of engineering students job postings from job recruiting websites. The growth dynamics along with the employment outcomes of engineering Using big data techniques .Techniques like lasso and random forest are made in use to optimize the API Keywords— Feature Selection, Hypothesis Testing, Job Salary, Prediction engine, Random Forest, Regression, Salary Predictors, Support Vector Machines, Salary Predictor

India has witnessed a rapid expansion in the higher education institutions and with this fast pace of growth day by day, competition has set in among the institutes. Before taking admission in an institution student assess the facilities and standard of the institution by referring to the website, other admission portals and by taking peer opinion and public perception. Educational institutions around the world are now requesting students’ feedback on all elements of academic life in the form of a satisfaction feedback questionnaire. The goal of this research is to describe the development and implementation of a survey to assess undergraduate and postgraduate student satisfaction. The Student Satisfaction Survey is a useful and effective instrument that tries to focus resources on areas, where there is low satisfaction but high importance. This paper gives detailed information about the methodology, calculation and outcome of the exercise utilizing Likert scale analysis. With the recent emerging trends, this innovative method offers flexibility to integrate more parameters, group certain parameters to get feedback on a particular issue and transmute the type of questions with changing environment and structure. The analysis is done using a statistical method. The survey result directly highlights the importance and flexibility of method to evaluate overall satisfaction, satisfaction cognate to a single parameter and satisfaction for questions grouped together underlining some paramount aspects of higher education.