Category Archives: Uncategorized

Authors: Komal Bhatkar, Gauri Gadhave, pragati Ingale, Ankita Gunjite, prof. Prachi Walunj

Abstract: The “Creating Wi-Fi Using Arduino Robot Car System” project focuses on the design and implementation of a smart robotic car that can be controlled wirelessly through a Wi-Fi network. The main objective of this project is to develop a low-cost, flexible, and user-friendly robotic system capable of remote operation using a smartphone or computer. The system utilizes an Arduino microcontroller integrated with an ESP8266 Wi-Fi module to establish wireless communication between the car and the user’s device. Through this setup, the user can send commands via a web-based interface or mobile application, which are then processed by the Arduino to control the car’s motion, such as forward, backward, left, and right movements.

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Authors: Balaganesh.S, Mrs.S. Indhumathi,M.E, Dr.A.Shiny Pradeepa, M.E

Abstract: Electric vehicles rely heavily on battery performance, safety, and lifespan, making efficient battery management essential. Existing battery systems face drawbacks such as inaccurate state estimation, poor thermal management, cell imbalance, and limited real-time fault detection, leading to reduced efficiency and safety risks. A Battery Management and Monitoring System addresses these issues by continuously supervising battery parameters to ensure safe, reliable, and optimal EV operation. Therefore, this project proposes a smart, connected, and predictive solution for effective battery management in electric vehicles. The system utilizes both an ESP32 and a Raspberry Pi Pico as central controllers to enhance data processing and control capabilities. Sensors such as voltage, current, and temperature (DHT11) are used to continuously monitor the battery’s key parameters. The ESP32 handles IoT connectivity, transmitting real-time data to a cloud platform (like Blynk), and allowing users to remotely monitor battery status and control motor operations via the internet. Meanwhile, the Raspberry Pi Pico is employed to manage local data acquisition, signal processing, and protective control logic. This division ensures faster and more reliable responses to critical conditions. A relay driver and electronic relay are used to regulate the DC gear motor, ensuring optimal power management based on the sensed data. In case of abnormalities such as overvoltage, overcurrent, or overheating, the system can automatically trigger protective actions to prevent battery damage. This intelligent and connected solution not only improves operational efficiency and reliability but also promotes the advancement of sustainable electric vehicle technology through smart, dual-controller energy management. The combined use of ESP32 and Raspberry Pi Pico provides both robust cloud integration and precise local control, making the system highly responsive and reliable.

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Authors: Diksha Pawar, Prof. Jayshree Boaddh, Prof. Rahul Patidar

Abstract: Alzheimer's disease (AD), the leading cause of dementia worldwide, affects more than 55 million individuals and gen-erates annual healthcare costs exceeding two trillion USD [14]. A substantial proportion (30–40% per year) of pa-tients with mild cognitive impairment (MCI) progress to AD [2], making early and accurate prognostication essential for timely intervention, trial enrichment, and resource allocation. This paper presents a comprehensive review of a re-cent longitudinal MRI-based study by Aghajanian et al. [1], which integrates three-dimensional (3D) convolutional neural networks (CNNs), time-aware long short-term memory (T-LSTM) networks with attention mechanisms, and radiomics features to predict MCI-to-AD conversion using structural MRI. The cohort comprises 228 ADNI MCI participants with at least three T1-weighted MRI scans over an 18-month window (684 scans in total) [1]. A 3D Res-Net-18 backbone [9] extracts volumetric features, fed into a T-LSTM incorporating inter-scan intervals and attention mechanisms [10]. The best longitudinal model achieves a concordance index (c-index) of 0.90, with time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year conversion prediction, respectively, and an approximate 11-fold hazard ratio between high- and low-risk groups [1]. This review analyzes the methodology, highlights its strengths and weaknesses, and discusses key implications for clinical translation.

 

 

 

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Authors: Dr. M. L Kiran, J. Divya, G. Vineetha, M. Mahitha, P. Likhitha

Abstract: The exponential growth of urban vehicular traffic has rendered traditional timer-based signal control systems inefficient, leading to increased congestion, fuel wastage, and carbon emissions. This paper proposes a novel Drone-Based Traffic Density Control System that leverages Unmanned Aerial Vehicles (UAVs) equipped with ESP32-CAM modules for real-time, aerial surveillance of road intersections. Unlike fixed infrastructure, the proposed system utilizes a rotating camera mechanism to provide 360-degree coverage, eliminating blind spots. The system employs Edge AI for vehicle detection and density estimation, transmitting telemetry data via ESP-NOW Protocol to a ground-based traffic controller. This paper presents the mathematical modeling of the traffic flow using Webster’s optimization logic and the PID stability analysis of the drone flight controller. Experimental results demonstrate that the system successfully adapts signal timing based on real-time density, significantly reducing average waiting time at intersections. In addition,the system incorporates emergency vehicle detection from the camera feed and immediately grants priority green to the corresponding approach, pre-empting the normal phase sequence to reduce emergency response time.

DOI: https://doi.org/10.5281/zenodo.18619404

 

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Authors: Karthikeyan R, Akshith G, Charukesh S, Monica Lakshmi R M.E

Abstract: This project develops a Spam Comment Detection and User Blocking System for a social media web application, designed to enhance user experience and maintain a secure online environment. Users can register, log in, send friend requests, chat, and post text or images, which may receive likes, dislikes, and comments. The system employs an advanced classifier algorithm to detect and filter negative or spam comments in both the chat and post sections. If a user exceeds 10 spam attempts, their IP address is blocked, preventing further access to the platform. Users can also create and share local events, which are visible to other users. The admin has oversight capabilities, including viewing user activity, managing events, and monitoring time spent on the platform through graphical analysis. The admin can also intervene by sending warnings to users displaying addictive behavior. The system integrates HAM algorithms and Bloom Filter data structures to improve spam detection efficiency and ensure optimal performance. This solution helps foster a safe, interactive environment by reducing harmful content and promoting responsible usage.

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Authors: Khushboo Mishra

Abstract: The combination of Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has transformed the practice of the modern agricultural sector by providing the possibility to monitor the crops precisely, make decisions immediately, and take care of the resources. Conventional agricultural practices tend to assume homogenous application of inputs and manual monitoring, which ignore spatial and temporal changes in the soil, climatic and crop conditions which result in wasteful utilization of water, fertilizers and energy. IoT based WSNs overcome this shortfall by supporting distributed sensor nodes that continuously gather environmental and crop related data such as soil moisture, temperature, humidity, nutrient level, and health of the plant. They have low-power microcontrollers (e.g., ESP32, Arduino, NodeMCU) and can be connected through wireless networks, including LoRaWAN, Zigbee, WiFi, and NB-IoT, sending data to wireless access points (gateways), and cloud or edge computing platforms to be processed and analyzed. Predictive insights, early alerts to crop stress, pest infestations, and nutrient deficiencies can be made through advanced machine learning models and edge AI with 92-95.9 percent success in environmental and crop condition prediction. According to performance reviews, there are vast energy efficiency improvements (up to 67 percent), resource use (water and fertilizer savings up to 40 percent), network reliability (PDR >95 percent), and crop yield (up to 30 percent). The selection of the protocol, hierarchical clustering (LEACH), and the low-power architecture make network lifetime and coverage to be optimized. The main issues are environmental interference, power constraint, security of data as well as interoperability between heterogeneous sensors and communication protocols.

DOI: https://doi.org/10.5281/zenodo.18617318

 

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Authors: Anand Kumar Gupta, Arshita Kumari, Swarangi Karangale, Shalni Kumar, Paramanand Kumar Bharti

Abstract: Objective: To systematically review and synthesize recent evidence (2020–2025) on the role of clinical pharmacists in type 2 diabetes management, focusing on clinical outcomes, patient education, adherence, and cost-effectiveness. Methods: Literature from PubMed, Scopus, and other databases (2020–2025) was reviewed, including randomized controlled trials, cohort studies, and systematic reviews examining pharmacist interventions in diabetes care. Results: Pharmacist-led interventions achieved significant reductions in HbA₁c (0.52 to 3.59%), improved patient adherence, and enhanced cost-effectiveness. Structured clinics such as DMTAC demonstrated consistent improvements in glycemic control and cardiovascular risk parameters. Conclusion: Clinical pharmacists enhance diabetes management through collaborative care, education, and therapy optimization, resulting in improved patient outcomes and reduced complications.

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Authors: Showkat Hussain Bhat

Abstract: Nowadays, the teaching and learning landscape is embracing a number of new pedagogical innovations and some of these involve the use of e-learning through Blended Learning (BL). This study attempts to assess the need of blended learning as an instructional paradigm to rejuvenate teaching. In this connection, it is substantial that innovative pedagogical approach must be embraced in the classrooms. Teaching classes could be completely combined together by using numerous synchronous and asynchronous gadgets. The way of fully integrating technologies could be helpful to increase styles of communication, mentor-learner engagement, learner satisfaction, academic motivation and performance of students. This study suggests that instructors could use blended learning pedagogy because students shifted to e-learning as an alternate to in-person classroom because of rising usage of smart phones because of anytime and anywhere class.

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Authors: K. Perarasu

Abstract: The stock market is a crucial component of the financial system, playing a significant role in economic development and wealth creation. Traditional financial theories assume that investors behave rationally and make decisions based on complete information. However, practical market observations reveal that investor behavior is often influenced by psychological, emotional, and social factors. This study aims to examine stock market trends and analyze how investor behavior impacts market movements. The research adopts an empirical approach using both primary and secondary data. Primary data is collected through structured questionnaires administered to individual investors, while secondary data is obtained from stock market indices, financial reports, and published research studies. Statistical tools such as percentage analysis, correlation, and graphical interpretation are employed to analyze the data. The findings reveal that investors frequently exhibit behavioral biases such as herd behavior, overconfidence, loss aversion, and risk aversion. Market trends show significant volatility during periods of economic uncertainty, indicating emotionally driven investment decisions. The study concludes that investor behavior plays a vital role in shaping stock market trends and that incorporating behavioral finance concepts can enhance investment decision-making and market stability.

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Authors: Chinmay Chouhan, Srashti Thakur

Abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

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