Authors: Mrs. M. Lavanya, Ms. R. Kavinila, Ms. M. Harini, Ms. K. Keerthana

Abstract: Education for students with disabilities continues to face challenges due to inadequate accessibility tools, lack of adaptive content, and poor connectivity in rural areas. Existing technologies such as screen readers, speech-to-text converters, and sign language translators function independently, resulting in fragmented learning experiences. EduAble, is a multi modal AI-powered learning platform designed to support students with visual, hearing, mobility, and neurodiverse challenges. It integrates Text-to-Speech (TTS), Speech-to-Text (STT), sign language and gesture recognition, and adaptive content simplification to create a unified, inclusive learning environment. EduAble is developed using Django with Django REST Framework for backend processing and React Native for cross-platform mobile accessibility, supported by PostgreSQL for data storage.The platform employs advanced AI models such as gTTS for speech synthesis, CNN with MediaPipe and OpenCV for gesture and sign language detection, and BERT for text simplification using TensorFlow which collectively enhance learning accessibility and provide a more effective and integrated assistive education system.

DOI: https://doi.org/10.5281/zenodo.18585544

Published by: vikaspatanker

Authors: Dr. Suman Dhawan

Abstract: Faculty Development Programs (FDPs) are very important to the careers of teachers in higher education institutions. They become better at what they do, and the entire institution is improved. But the fact is that faculty members are not all alike, and age is actually a factor in what they want from an FDP. This research explores how the interests of faculty change with age. On the basis of a structured survey of 302 faculty members from various universities and colleges, a One-Way ANOVA test was conducted to determine how needs differ in three age groups: 25-34, 35-44, and 45 and above. The findings are quite striking. Age does make a difference. The younger generation is more concerned with handling classes and establishing a sound foundation in subject matter. The middle-aged faculty begin to tilt towards competency development and professional growth. The 45+ age group is more concerned with developing their personalities and management acumen. To synthesise all this, this study proposes an Age-Life-Cycle Model of Faculty Development Priorities. The study concludes that "one-size-fits-all" solutions do not work. If universities are serious about faculty development, they need to listen to where people are in their life cycle and provide development that fits.

DOI: http://doi.org/10.5281/zenodo.18584290

Published by: vikaspatanker

Authors: Delos Santos, Greg, Galgo, Lady Nathalie A, Gubat, Karyll D., Zamora, Daisy Anne

Abstract: The purpose of this study is to design and develop a soil testing device known as TerraGrow using IoT technology that could help farmers test soil properties and recommend the appropriate crops for growing. The soil testing device could measure EC values, soil moisture levels, and soil temperatures to acquire valuable soil information, which could then be interpreted using a mobile or web application. The results obtained were analyzed using mean and percentage to test the accuracy of the soil testing device. The results revealed that the soil testing device TerraGrow could measure and interpret soil properties with greater accuracy and efficiency. The application of IoT technology made it easy for the soil testing device to store data and provide recommendations for growing appropriate crops based on soil quality. The study results showed that the soil testing device TerraGrow could work with greater efficiency and ease compared to traditional methods. The study concluded that the application of TerraGrow has made a significant contribution to modern agricultural practices. The soil testing device could be made even better with suggestions like automatic calibration and solar power.

DOI: https://doi.org/10.5281/zenodo.18539454

Published by: vikaspatanker

Authors: Riya Jaiswal, Simran Sahu, Prince Pandey, Vandana Thripathi

Abstract: Heart disease continues to be a major global health concern, accounting for a significant number of premature deaths each year. Early detection can improve survival rates, yet traditional diagnostic methods are time-consuming and often dependent on expert interpretation. This study applies machine learning techniques to clinical data to develop a predictive model capable of estimating heart disease risk. Various algorithms—including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—were evaluated. The results show that ensemble models deliver the highest accuracy, demonstrating strong potential for supporting clinical decision-making.

Published by: vikaspatanker

Authors: Sanjana Sunilkumar Dubey, Dr Vipin Kumar

Abstract: Education of school and college students on climate change is highly important in influencing mitigation and adaptation behaviours in the long term especially on the climate prone countries like India. This research is a survey-based evaluation of climate change awareness, risk perception, self efficacy, and pro environmental behavioural intention among Indian students, with a special interest in the variations of these variables according to the urban and rural geographical location, the type of school, and the exposure to climate education programmes. The questionnaire comprised a structured questionnaire that was delivered through a stratified sampling design to the participants that were secondary school students (Classes 912) and first-year undergraduate programmes. The measure consisted of climate knowledge, perceived risk, self-efficacy, behavioural intention, and information sources on climate. The analysis of data was done using descriptive statistics, group comparison, and multiple regression modelling to determine the predictors of behavioural intention toward climate action. Using an exemplary sample size (N = 600), the findings show that, although students will exhibit average knowledge of climate as a whole, there exist significant disparities in knowledge of health-related climatic effects and locally applicable strategies of adaptation. Students in urban areas always claim more knowledge and perception of risk of the climate than rural students due to the information availability and access to education. The results also indicate that perceived risk and self-efficacy have a stronger effect on behavioural intention than knowledge does. Being members of eco-clubs and having undergone climate-focused school-based climate modules are both substantially linked with intentions to participate in climate-positive behaviours.

Published by: vikaspatanker

Authors: M.Selvam, Dr A.Shiny Pradeepa

Abstract: Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. Rapid urbanization and the continuous growth of vehicle population have placed immense pressure on existing traffic management systems. Conventional traffic surveillance methods, such as fixed cameras, loop detectors, and manual monitoring, often suffer from limited coverage, high infrastructure costs, and lack of real-time adaptability. Therefore, this project proposes a drone-based traffic surveillance system operates through the coordinated functioning of power, sensing, control, communication, and actuation modules. The system is powered by a 3.7V Li-ion/Li-Po battery, which supplies energy to all onboard components through a battery protection and charging circuit to ensure safe and stable operation. The flight controller acts as the central processing unit, receiving real-time data from sensors such as the gyroscope and accelerometer to maintain flight stability, orientation, and balance. Front and bottom cameras capture aerial and ground-level traffic footage, which can be switched using the camera switching module depending on surveillance requirements. The optical flow sensor assists in position holding and low-altitude navigation. User commands are transmitted via a 2.4 GHz transmitter and receiver, enabling remote control and mission updates. Based on sensor inputs and control commands, the flight controller generates appropriate signals to the motor driver, which regulates the speed of the coreless DC motors for precise manoeuvring. Additionally, the LED lighting module enhances visibility during low-light or night-time operations. Through this integrated workflow, the drone efficiently captures real-time traffic data while maintaining stable and controlled flight.

Published by: vikaspatanker

Authors: Ms.Walunj P.D, Ms.Sangme S.R, Ms.Suryawanshi P.P, Ms.Hanumante K.B, Ms. Upase M.S

Abstract: This paper presents the design and implementation of an OTP (One-Time Password) based door lock system using Arduino. The system enhances security by allowing access only after successful OTP verification. The OTP is generated and transmitted to the authorized user via a GSM module. The proposed system is low-cost, reliable, and suitable for homes, offices, and restricted areas. Experimental results show that the system provides improved security compared to traditional lock systems. Security of residential and commercial premises is a major concern in today’s world. Conventional locking systems such as mechanical keys and password-based locks are vulnerable to theft, duplication, and unauthorized access. To overcome these limitations, this project presents an OTP (One-Time Password) based door lock system that provides enhanced security and flexibility. The proposed system generates a unique, time-limited OTP for every access request, which is sent to the authorized user’s registered mobile number through a GSM module. The user must enter the received OTP using a keypad or mobile interface to unlock the door. A microcontroller controls the verification process and activates a relay to operate the electronic door lock. Since the OTP is valid for only one use and for a short duration, the chances of unauthorized entry are significantly reduced. The system is simple, cost-effective, and suitable for homes, offices, and restricted areas, offering a reliable solution for modern security needs.

Published by: vikaspatanker

Authors: Sushovan Chandra, Barsha Maity, Swagatam Biswas, Angshuman Ghosh, Angshuman Ghosh

Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, influencing nearly every sector of society. From healthcare and education to finance and governance, AI-driven systems are reshaping how decisions are made and services are delivered. Despite its growing adoption, public awareness and understanding of AI remain limited, particularly regarding its risks and ethical challenges. This research paper examines the positive and negative impacts of AI, highlights key ethical and social concerns, and emphasizes the importance of awareness, regulation, and responsible implementation. The study aims to provide a balanced perspective on AI, encouraging informed usage that maximizes benefits while minimizing harm.

Published by: vikaspatanker

Authors: Shani Singh

Abstract: Additive manufacturing (AM) has evolved from a rapid prototyping technique into a key production technology for complex, high-performance components in aerospace, automotive, biomedical, and energy sectors. However, the mechanical reliability of AM parts remains highly sensitive to process parameters, thermal history, and defect formation mechanisms. Traditional empirical and physics-based models struggle to describe the nonlinear and multidimensional interactions that arise in AM processes, limiting their ability to predict strength and structural integrity. Machine learning (ML) has emerged as a powerful alternative, capable of learning complex process–property relationships directly from data and providing accurate predictions for tensile strength, porosity, surface roughness, hardness, and dimensional accuracy. This review synthesizes recent advances in ML applications across polymer-, metal-, and ceramic-based AM technologies, focusing on process parameter analysis, mechanical strength prediction, defect monitoring, and parameter optimization. The discussion highlights commonly used ML algorithms, sensor integration strategies, and hybrid optimization approaches, and identifies key research gaps related to dataset scarcity, model generalization, interpretability, and cross-platform reproducibility. Finally, the review outlines future directions, including digital twins, physics-informed ML, and reinforcement learning, to enable autonomous, industrial-grade intelligent additive manufacturing systems.

DOI: https://doi.org/10.5281/zenodo.18494934

 

Published by: vikaspatanker

Authors: Sampang, Althea Mae A., Halik, Iederf Sean B, Labadan, David Andrew, Canada, Kevin Raymart C

Abstract: The researchers aimed to develop a multi-sensor automated system for mosquito detection and environmental surveillance, Mosquitect, having a purpose of offering support in the early assessment of dengue risks in areas prone to mosquito presence. MosquiTect is designed with the use of an Arduino UNO R4 Microcontroller that aids in tracking wingbeat frequencies, temperature, humidity, and visual-based detection of mosquitoes through a camera module. The data analysis is performed by utilizing percentages and means. During the 14-day testing, the findings show that MosquiTect had a 97.64% of success rate in terms of detecting wingbeat frequencies and gender identification signals; temperature and humidity provided a 100% success rate in monitoring environmental parameters; and a 77.07% success rate in terms of visual-detection of mosquitoes. The result shows that MosquiTect holds high relevance in the facilitation of preventive steps against dengue, especially in tropical areas. MosquiTect also possesses strong practicality for aiding governmental departments in forming preventive measures for dengue. This cultivates improvements in the capability of optical detection and image recognition, energy efficiency, environmental surveillance, and predictive modelling for the population of mosquitoes and their potential dengue outbreaks by the public health agencies.

DOI: https://doi.org/10.5281/zenodo.18494907

Published by: vikaspatanker