Authors - Suresh V Reddy, Sanjay Bhargava Abstract - Cybercrime on social media platforms such as Facebook and Twitter has emerged as a significant challenge due to the open, interactive nature of these platforms. Various machine learning (ML) and deep learning (DL) techniques have been deployed to detect different forms of cybercrime, including phishing, spamming, hate speech, and identity theft. This paper provides a comparative analysis of these approaches, focusing on their application to cybercrime detection on Facebook and Twitter. Through a detailed literature review, we evaluate the strengths and weaknesses of these techniques, considering their performance and scalability. Moreover, the ethical challenges and the need for privacy-preserving mechanisms are discussed, along with future directions for research.
Authors - Rakesh Babu B, Rajesh V, Syed Inthiyaz, Srinivasa Rao K, Sri Sravan V Abstract - Brain tumours are life-threatening disorders with significant fatality rates. Patients have a higher chance of survival when brain tumours are diagnosed early and treated more effectively. Therefore, for the purpose of better and boost the early identification of brain tumours, computerized segmentation as well as classification techniques are needed. It is possible to safely and promptly detect tumours using brain scans such as computed tomography (CT), magnetic resonance imaging (MRI) and other techniques. Revolutionary changes have occurred in many different disciplines as a result of recent developments in artificial intelligence (AI). AI models are becoming essential tools for interpreting images in bio medical field. Deep learning is one of these that signifies extraordinary capacity to deal with enormous data collection, revolutionizing numerous fields in the biomedical profession. This article evaluates a state-of-the-art AI based segmentation and classification system and discovers major classes for brain tumours. The potent learning capability and effectiveness of AI approaches have been assessed. Convolutional Neural Network (CNN) is one of the AI subfields that has demonstrated remarkable performance in analysing medical imagery. Consequently, the processing of medical imagery, particularly brain MRI images, was the main emphasis of this review paper, which also examined different deep learning model architectures in addition to CNN.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari, Janhvi Shirbhate, Sharvari Pipare Abstract - Artificial Intelligence (AI) is increasingly being hailed as the key to the future of healthcare supply chain management in countries such as India, where healthcare is a particularly complex setting for an integrated supply chain. This review presents the various Data-driven Artificial Intelligence (AI) technologies such as Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and Robotic Process Automation (RPA) that help in the automation of essential processes like demand forecasting, inventory management, and cold chain logistics in an efficient and timely manner. AI helps deliver vital supplies on time and minimizes any disruptions of services by utilizing predictive analytics and real-time monitoring. However, high implementation costs, data privacy concerns, the need for integration with legacy systems, and a need for more skilled professionals are barriers to the adoption of AI computing. To extract the maximal potential AI can offer healthcare logistics, the issues above need to be addressed. Upcoming research directions include further development in quantum computing, IoT integration, and collaborative AI platforms to fulfil resilience and sustainability objectives for supply chains. The results underscore the potential of AI to transform health supply chains and provide an opportunity to realize more scalable, responsive, and efficient health services.
Authors - Rajeshree Khande, Sachin Naik, Akshay Tayade, Amar Kale, Kunal Phalke Abstract - The authors propose for the LSTM-XGBoost model for portfolio optimization as well as stock price prediction. The model has incorporated the benefits derived from XGBoost, a gradient-boosting algorithm that enhances the ability of a model to predict structured and improved data, and Long Short-Term Memory (LSTM) networks, which excel at characterizing time-series data based on temporal relationships. The XGBoost model takes advantage of the LSTM model by utilizing the anticipated outputs it makes for improving the precision and overall efficiency of the model while the LSTM model is designed to work with ordered data peculiar to stock markets specifically on patterns and trends over time. In the study authors employ this type of hybrid to determine variables such as volatility and the moving average of historical stock price index of NIFTY50. The authors have obtained total model accuracy of 98.33%. Authors also use the Sharpe ratio to maintain an optimal portfolio because it shows investors the optimal ratio of expected stock returns. This research contributes to enhancing financial forecasting by integrating deep learning and machine learning techniques, ultimately offering the formulation of a new risk avert portfolio as well as stock price prediction.
Authors - Jaiditya Nair, Sunil Kumar Abstract - The increasing demand for AI-driven solutions in development has encouraged people to conduct various research into generating code from natural language prompts. My paper presents a Retrieval-Augmented Generation (RAG) pipeline for code generation, making use of embedding models, contextual retrieval, and advanced language models such as Mistral and CodeLLama. This approach incorporates document indexing and metadata extraction to create context-aware code snippets and at the end of the process, we get a python file with the generated code present in it.
Authors - Nishita Shekhar Bala, Sree Vani Bandi, Stephen R, Ravi Dandu, Balakrishnan C Abstract - These days internet is became an essential part of human life and affects various domains which includes education, business, social interactions, mental health. It pushes the society ahead through increasing innovations, amplifying learning techniques, connecting people across the globe and access to vast resources which makes it a valuable tool in this modern society. But it comes with problems such as internet addiction, sleeping disorders, health complications. This abstract discusses about dual impact of internet uses, focusing on its significant benefits and possible dangers. Hence, there is need to mange use of internet so one can make use of its benefits at the same time reducing the affects which are caused by internet on human life.
Authors - Chandan Raj B R, A. Yasaswi, Deepika K, Uday Bhaskar Reddy, Delina Yadav K, Joshna K Abstract - It is quite difficult to communicate with deaf individuals. This article extends the complexity of Indian Sign Language (ISL) character classification. Sign language is insufficient for the hearing and speaking disabled. Hand gestures of disabled individuals may appear confused to those who have not learnt the language. Communication should be two-way. In this essay, we will discuss how to learn a language through sign language. Images are processed using computer vision processes, including grayscale conversion, dilation, and masking. We employ Convolutional Neural Networks (CNN) to train and recognize images. Our example has an accuracy of approximately 95%. Gestures serve as a nonverbal communication tool in language. People with hearing or speech difficulties frequently utilize them to communicate with others or among themselves. Many loudspeakers are created by various manufacturers around the world. This study demonstrates that many experiments are undertaken each year, with several articles published in journals and conferences, and that research on vision-based gesture recognition is ongoing. Cognitive navigation focuses on three areas: information retrieval, environmental information, and gesture representation. In terms of identity verification, we also evaluated the authentication system's effectiveness. The physical movement of the human hand generates gestures, and gesture recognition contributes to improvements in autonomous vehicle operation. This paper use the convolutional neural network (CNN) classification technique to detect and recognize human motions. This workflow consists of region-of-interest coordination via masking, finger segmentation, normalization of segmented finger pictures, and finger recognition using a CNN classifier. Use the mask to separate the hand portion of the image from the rest of the image. The histogram equalization approach is used to improve the contrast of each pixel in an image. This work uses a variety of scanning techniques to classify fingerprints from hand photographs. The segmented fingers from the hand image are put into the CNN classification algorithm, which separates the image into different groups. This research proposes gesture recognition and recognition methods based on CNN classification, and the technology achieves good performance using cutting-edge methodologies.
Authors - Anudeep Arora, Ranjeeta Kaur, Neha Tomer, Vibha Soni, Neha Arora, Anil Kumar Gupta, Lida Mariam George, Prashant Vats Abstract - The incorporation of data analytics into internal audit operations is a noteworthy progression in augmenting the efficacy and productivity of audits. In this paradigm, strategic analysis refers to using data-driven insights to evaluate risks, expedite audit procedures, and enhance organizational controls. This article examines the use of strategic analysis in data analytics and internal audits, including important techniques, advantages, and difficulties. It talks about how sophisticated data analytics methods, such as machine learning, statistical analysis, and visualization software, can change the way that auditing is done today. In addition, the paper looks at case studies and potential future developments in the subject, giving readers a thorough understanding of the various ways internal auditors might use data analytics to provide audit results that are more precise and useful.
Authors - Artika Singh, Manisha Jailia Abstract - Effective management of infectious disease outbreaks rely heavily on informed decision-making processes. There are many approaches given for decision-making some of them are expert decision-making, creative problem solving, public engagement, and decision-making under deep uncertainty (DMDU) in outbreak management (OM). The integration of these aspects is critical to enhancing the responsiveness and efficiency of public health interventions. This paper discusses the current state of expert decision-making processes, the role of creativity in managing complex situations, the impact and challenges of incorporating public and patient engagement (PPE) in OM. The paper concludes with recommendations for future research and practice to improve outbreak management strategies.
Authors - Prateeksha P Malagi, Priyanka R Patil, Shamshuddin K G, Suneeta V Budihal Abstract - The advent of blockchain technology presents a transformative opportunity for enhancing the integrity and efficiency of voting systems. This paper explores the design and implementation of a blockchain-based voting system aimed at addressing common challenges faced in traditional electoral processes, such as voter fraud, lack of transparency, and low participation rates. By leveraging the decentralized and immutable nature of blockchain, our proposed system ensures secure voter authentication, real-time vote tracking, and tamper-proof record keeping. The study outlines the technical architecture, including smart contracts and cryptographic techniques, while evaluating the system's performance through simulated voting scenarios. Furthermore, we discuss the implications of this technology for promoting democratic engagement and restoring public trust in electoral outcomes. Our findings suggest that a blockchain-based voting system not only enhances security and transparency but also offers a scalable solution to modern electoral challenges.
