Authors - Nisha Fulzele, Chetan Parlikar Abstract - The development of financial instruments has greatly changed consumer expenditure patterns, and credit cards have been central in contemporary economies This paper analyzes the expenditure behavior of credit card customers in Wardha City, with reference to priority drivers of expenditure patterns. Employing a descriptive research method, primary data were gathered from 140 participants using a systematic questionnaire. Analysis proves that young professional salaried individuals constitute the maximum segment of credit card customers, who prefer online payment and high-end transactions. Whereas convenience and payment flexibility come with credit cards, their use in everyday consumption is still limited. Correlation analysis indicates that rewards, cashback, impulse buying, and financial security drive spending most, compared to peer influence and promotional offers, which have lesser impacts. The research indicates that credit card use in Wardha City is increasing, driven mostly by electronic payment behavior and financial stability. By comprehending these behavior patterns, financial institutions can make strategies to encourage prudent use of credit and financial literacy among consumers.
Authors - Radhika V. Kulkarni, Vaibhav Aher, Harsh Ukey, Sujal Dubey, Aarya Labhshetwar, Manjiri Kulkarni Abstract - The majority of community in globe use sign language as the most basic way of interaction with Deaf and speech-impaired people. In most instances, a person finds it difficult to learn sign language for communicating with deaf and dump people, which leads to isolation among those individuals. Most people are unaware of the interpretations made in sign language. Hence, this paper presents an intelligent sign recognition system for translation of dynamic sign language for easy communication among people with hearing and speech impairments. The intelligent system takes advantage of advanced computer vision and deep learning techniques to identify dynamic hand signs accurately. This approach includes video data capture, preprocessing, feature extraction, and real-time gesture recognition. Hand movements are captured from webcam video streams, and the MediaPipe library is used to capture key points over the hand. A sequential model based on deep learning maps the relationships in hand gestures, which ensures high recognition accuracy. Extensive testing on different hand gesture recognition datasets shows that they perform efficiently and reliably in real-world situations. This technology facilitates greater accessibility through the ability to quickly and accurately translate sign language, thereby helping create inclusive communication technologies.
Authors - Molly Goel, Prince Kumar Sharma, Nainshi Singh, Madhvi Gaur Abstract - A secure and dignified electronic voting system is needed to provide the security and decency of a traditional one. While still allowing for flexibility and accuracy, this system has been tested for a long time. The use of blockchain technology can be utilized to actualize distributed voting structures. Despite the technological advancements that have occurred in the past few years, the traditional balloting system still remains unsuited for the modern era. There are numerous issues that prevent the integrity of the elections, such as the lack of transparency and the use of bribes. Besides these, the time it takes to check the vote's integrity is also very long. Current technology has to be used to improve the voting system. One of the most important factors that needs to be considered is the development of blockchain technology. This type of innovation eliminates the character flaw in the voting process and ensures that the correct votes are sent out. The development of blockchain technology is carried out through a stable set of rules that are designed to solve the problems related to the voting process. This type of innovation will help to ensure that the public can easily remember the individuals who participated in the process. The development of a voting poll programming application can help the political selection executives and citizens get the most out of it. However, it can also expose them to various risks. For instance, e-voting can lead to political race safety issues and fraud. Despite the advantages of this type of innovation, it is still not ideal for the people who are interested in maintaining a transparent and honest political selection process.
Authors - Manisha Mane, Saurav Bedse, Vikrant Patil, Pruthviraj Dhande, Om Darekar Abstract - The fast advances in deep learning and computer vision have dramatically improved the ability to detect objects, with applications in surveillance, driverless cars, and smart traffic management. The current paper describes an implementation of the YOLOv8 model for real-time object detection on different categories such as persons, cars, and bicycles. We trained the model on a customized dataset of annotated images, fine-tuning it through extensive hyperparameter tuning and multiple training epochs. Our training setup consisted of 75 epochs, utilizing a Tesla T4 GPU for computation. The model recorded a mean Average Precision (mAP@50) of 76.5% over all classes, with class performance highlighting high precision and recall rates for classes like cars (98.2%) and bicycles (87.8%). To further improve accuracy, we utilized data augmentation methods, batch normalization, and optimizer tuning. After training, the model was subjected to extensive validation, with an inference speed of 8.5ms per image, making it viable for real-time performance. We also incorporated the model into a realistic deployment pipeline, showcasing its efficacy in real-world applications. This paper presents a thorough analysis of the trained model, such as performance metrics, comparison with other versions of YOLO, and discussion of future improvements. Our results emphasize the model’s ability to achieve speed and accuracy balance, rendering it an appropriate choice for object detection in real-time applications. Future research will investigate additional optimizations such as light-weight model variants and domain-specific dataset adaptation.
Authors - Abhay Shinde, Ketal Patil, Nirmitee Chaudhari, Samrudhi Bachhav, Kavita Moholkar Abstract - The energy that never goes out of style is solar energy that is readily available and produces no pollution; its use has increased over the years. It is an endless supply of energy. Optimising solar radiation absorption for power generation is still a major challenge. A solar panel's best position for collecting sunlight is orthogonal to the trajectory of the sun's rays, but throughout time, the sun's rays direction varies. Even though a solar tracking system does a good job of recording the sun's motion during the day, it suffers when adverse weather conditions cause the sun's intensity to decrease. A passive tracking system, which can handle such circumstances and yield better results, can therefore be employed to overcome them. The design and functionality of a solar tracking system are the topics of this research. By aligning the solar panel with the sun's position, which is grounded on a fluid medium, the suggested outcome offers the best possible conversion of solar energy into electrical power.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Trupti Chetan Kherde, Dhiraj Jitendra Marathe, Prathamesh Shivaji Kadam, Sanskar Dipak Shinde, Chetan Balaji Phulmante Abstract - Agriculture is one of the fundamental pillars of human civilization. In addition to providing food, it boosts the economy. Crops and plant leaves are susceptible to several diseases during agricultural production. Diseases prevent each species from growing. Early and accurate plant leaves disease diagnosis helps to minimize major damages to plants. Plant leaves disease classification and detection has grown to be major issues. Failure to promptly identify and categorize plant diseases could lead to agricultural plant loss and a sharp decrease in product. Utilizing digital image processing techniques in their fields can help farmers enhance output and decrease losses. Various techniques have been developed and implemented to identify and classify plant diseases. Over the years, considerable advancements have been made in finding different disease by exploring and applying different methodologies. However, because of new developments, and conversations, improvements are needed. Globally, crop production can be greatly increased with the application of technology. Conventional techniques, such as laboratory-based diagnostics and manual inspection, are still dependable but time-consuming and labor-intensive. Emerging technologies, such as Machine learning (ML) and deep learning (DL) techniques have revolutionized automated disease detection, offering robust solutions for analyzing complex patterns in plant images. This survey highlights recent advancements in these areas.
Authors - Jayasmita Kuanr, Deepanjali Mishra Abstract - Dalkhai is conventionally a female-centric folk tradition; nonetheless, patriarchal frameworks have frequently influenced its performance and distribution. Grounded in Judith Butler's theory of gender performativity, which analyses how Dalkhai's lyrical narratives and physical expressions formulate, contest, and navigate gender identities. The emergence of digital media has allowed Dalkhai to explore new avenues of representation, enhancing reinterpretations of old themes and promoting wider interaction. Digital media and technology-enhanced performances have elevated female voices, but they may also commodify or alter traditional expressions to conform to modern cultural norms. This study contends that although digital technology provides opportunities for transformation and inclusivity, it also requires critical awareness about the recontextualization of traditional folk narratives in virtual environments. The study indicates that the convergence of gender performativity and digital media is transforming Dalkhai’s cultural relevance, establishing a dynamic arena for both continuity and transformation. The technology integration and folk traditions such as Dalkhai can transform while preserving their artistic integrity, providing novel opportunities for female representation in the digital era. Therefore, the study examines the changing performance of Odisha’s Dalkhai folk music via the perspectives of gender performativity and digital transformation. It proposes a critical textual and performative examination of Dalkhai's lyrics, gestures, and vocal expressions to elucidate how the folk tradition both reinforces and subverts gender stereotypes.
Authors - G.B.Sambare, Prajwal Solase, Raj Lokhande, Chaitanya Shinde, Sujit Aher Abstract - Heterogeneous wireless networks face challenges in ensuring smooth mobility between WLAN and LTE, as traditional handover decisions based on signal strength often degrade service quality. A more advanced approach incorporates multiple network parameters like signal power, link speed, system delay, and user mobility for optimized vertical handover. Real-time throughput calculations and dynamic network ranking enhance selection, while MCDA techniques improve transfer continuity, reduce delays, and minimize packet loss. Simulation results confirm that this strategy outperforms conventional methods by reducing handover failures and improving network selection. Additionally, advanced techniques like FSHO and SSHO are explored for seamless multimedia services in 5G networks.
Authors - Khushi Ingalalli, Vanshika Kavi, Sainath Walthati, Satish Chikkamath, Suneeta Budihal, Sujata Kotabagi Abstract - With the millions of tweets per day, Twitter is a rich and large database of information on public sentiment on a wide range of issues, including events, products, politics, and social issues. The purpose of this research is to create an automated system that can analyze tweet sentiments to determine attitudes as positive or negative. Through Natural Language Processing (NLP) methods and machine learning algorithms, the system efficiently handles high quantities of unstructured data, making sentiment classification possible in real time. The model begins the analysis by gathering various tweets from various sources, such as hashtags, user mentions, and trends. The tweets are then subjected to preprocessing techniques like removing stop words and treating misspellings, emojis, and special characters. Various classification models, like Naive Bayes, Support Vector Machines (SVM), Logistic Regression (LR) were experimented with to see which was most efficient in sentiment classification. Of these, Logistic Regression (LR) showed the best performance with an F1 score of 0.833 and accuracy of 83%. The efficiency of various feature extraction methods, such as Term Frequency- Inverse Document Frequency (TF-IDF) and word embeddings, was also examined to try and improve model performance. This work emphasizes the increasing importance of Twitter Sentiment Analysis across different fields, such as market research, event tracking, and social research. Sentiment analysis is employed by companies to know customer views and enhance services, whereas policymakers utilize it for measuring public reaction. By combining NLP and machine learning, the suggested system provides better and scalable method for sentiment analysis[1].
Authors - Gopal D. Upadhye, Deepak T. Mane, Devang Gentyal, Chetan Channa, Shubham Landge, Radhika Gadewar Abstract - Toxic comment identification in Hinglish (a combination of Hindi and English) is a difficult task because of code-switching, transliteration, and class imbalance. This paper suggests a machine learning based method for identifying toxic Hinglish comments based on TF-IDF feature extraction along with an ensemble model. In order to mitigate class imbalance, Random Oversampling was utilized, and model interpretability was facilitated using SHAP (Shapley Additive Explanations). The suggested model was trained on publicly released datasets, with 90.0% accuracy compared to individual classifiers. This work contributes to content moderation system for code-mixed languages and offer an extensible solution for social media toxicity detection.
