Authors - Sujith Kumar Banda, Ramzan Shareef, Swathi Sowmya Bavirthi, Mohammed Arbaz Ahmed Abstract - While meetings help make company decision-making more effective, documenting and distilling the material turns out to be a lot of time-consuming work and may also contain mistakes. The project provides an automated way of transcribing audio recording of meetings into text and applying NLP for perfect creation of useful summaries. As opposed to the existing techniques that resort to either means of human beings or platform-specific ones, our solution is a versatile way that can handle transcripts from a variety of online resources. This is a system that offers both abstractive and extractive summary techniques in the form of developed transformer models, such as BERT, to form logical summaries and TF-IDF and TextRank to focus the most important points in the summary. A wider applicability of Named Entity Recognition (NER) and Part-of-Speech (POS) tagging will allow summarization over key elements, including decisions taken and responsibilities assigned. The approach aims to make the capture of output from the meeting more efficient and reliable by automatically summarizing proceedings in meetings. User input and ROUGE scores will assess how well the system performs and guarantees quality useful summaries to stakeholders.
Authors - Rajeev Sharma, Santanu Sikdar, Govind Murari Upadhyay Abstract - To protect network infrastructure from new vulnerabilities and security dangers caused by the rapid growth of Internet of Things (IoT) devices, robust and adaptable Intrusion Detection Systems (IDS) are necessary. Due to their limited scalability and reactivity to different attack patterns, conventional intrusion detection systems (IDS) struggle to meet the unique demands of Internet of Things (IoT) networks. The novel Intrusion Detection System introduced in this paper is based on deep learning and is tailor-made for Internet of Things (IoT) environments. It employs complex neural network topologies to enhance the accuracy and efficiency of detection. Regarding the massive amount and variety of data generated by IoT devices, our suggested method improves performance without compromising detection accuracy by combining feature selection and dimensionality reduction strategy. Standard IoT network datasets were used for training and validation, with several assaults implemented to ensure comprehensive threat coverage and practical applicability. The results of the experiments show that the proposed system outperforms the state-of-the-art machine learning-based intrusion detection systems in detection accuracy, false positive rates, and scalability in contexts with limited resources for the Internet of Things.
Authors - Rutuj Barudwale, Vijeyandra Shahu Abstract - This article focuses on attempting artificial intelligence in stock price forecasting. Common stock market predictions and their prices can be assessed using dual primary analytical models known as technical and fundamental analysis. I employed a technical analysis of price trends predicting price movements using regression machine learning (ML). For instance, predicting how the price of a particular stock will close at the end of today based on historical price trends. In contrast to this approach of technical analysis, fundamental analysis can be applied to supervised machine learning algorithms to assist with identifying how news and social network users appear to be for or against certain entities. In the technical analysis, the historical price trends are retrieved from Yahoo, and in the fundamental analysis, the stock market tweets are analyzed to assess how the public feels about the stock prices. The findings portray an average performance; therefore, given the present environment of - technology, it is rather optimistic to presume that technology will ever beat the stock market consistently.
Authors - Satvik Taviti, Srreyasri Kurlagunda, Nandikanti Sri Gayatri, R M Krishna Sureddi, Raman Dugyala Abstract - Electronic Health Records (EHR) are considered to be amongst the most crucial elements for exchanging data in healthcare services. Thus, security for these records is the keystone of patient privacy and easy cooperation between the service providers. This review looks at four primary approaches to EHR security and predictive management: Blockchain, Attribute-Based Encryption (ABE), Deep Learning, and Access Control Models. Blockchain ensures data integrity, transparency, and traceability but scalability issues, high transaction costs, and interoperability challenges prevent its widespread adoption. ABE is appropriate for fine-grained access control in data sharing under the patient-centred approach but cumbersome and resource-intensive for managing encryption across the large healthcare network. Deep learning helps predictive analytics, personalized medicine, but with high computational demands that affect its real-time application in the clinical environment. While in terms of data confidentiality protection, models such as Role-Based or Attribute-Based Access Control may ensure proper restriction of authorized access, they might not suffice for dynamic, multi-provider health environments. Comparing the techniques will outline their relative merits, weaknesses, and security considerations, thus helping to understand how safe yet scalable systems for EHR storage could be built.
Authors - Edidiong Akpabio, Supriya Narad Abstract - This review aims to understand the integration of two emerging technologies: artificial intelligence and the Internet of Things. IoT is defined as the capability of implementing connections between regular items and industrial apparatuses that can liaise in real-time, exchanging and analyzing data. AI is an ideal companion to IoT in the sense that it brings decision-making into the equation and boosts the effectiveness and functions of IoT systems. This paper aims to review the use of AI and IoT in various fields, namely, smart cities, health, farming, and transport. In smart cities, IoT and AI applications are also applied to enhance traffic, energy consumption systems, and urban design. It has changed the way that the healthcare industry operates through better methods of patient monitoring, performance analysis, and telehealth. In agriculture, IoT sensors help monitor the effectiveness of crop management and the use of AI-based automation. It also covers the implementation of AI and IoT in autonomous vehicles, particularly the use of sensors for data processing, decision-making, and real-time data communication. However, the use of AI and IoT has some limitations, such as data limitations, security and privacy, and environmental impact. Indeed, the paper dwells upon these issues and provides the outlook for further research regarding edge AI, IoT sustainability, and the further evolution of the connections. With technological progress still in the process of evolving AI and IoT, future advancements hold more potential in terms of creating better connected, efficient, and sustainable solutions, not to mention the fact that AI is capable of solving existing challenges.
Authors - Rydhm Beri, Parul Sachdeva Abstract - The advent of IoT technology has significantly transformed the industrial sector, paving the way for the emergence of Food Industry 4.0. This research explores the integration of edge–cloud computing and IoT to create a smart framework tailored for the food industry. Central to this framework is the appli-cation of a Bayesian belief network (BBN) on an edge–cloud platform, enabling data-driven insights into food quality. The framework assesses data to calculate the Probability of Food Quality (PFQ) and utilizes the Food Quality Analysis Measure (FQAM) to evaluate food outlets. A bi-level decision-tree model further enhances the evaluation process by providing an in-depth analysis of food quality metrics. To address concerns around data security, blockchain technology is implemented, ensuring the protection of food-related information. The model is rigorously tested on a comprehensive dataset encompassing 43,520 instances from four restaurants. Simulation results highlight its high performance, achieving a temporal delay of 96.43 seconds, and the system demonstrates an accuracy of 98.93%, showcasing its robustness in real-world applications.
Authors - Rydhm Beri, Parul Sachdeva Abstract - Plant diseases present a serious threat to all forms of life. Early detection is vital which allows farmers to take prompt action, improving both their response and productivity. Our research centers on five common rice leaf diseases—bacterial leaf blight, leaf blast, brown spot, leaf scald, and narrow brown spot—along with a category for healthy leaves. Additionally, we examine two types of betel leaves: healthy and unhealthy. This study propose an innovative deep ensemble model that combines the EfficientNetV2L, InceptionResNetV2, and Xception architectures. This model addresses issues of underfitting and performance by utilizing advanced techniques including data augmentation, Global Average Pooling, preprocessing, Dropout, L2 regularization, PReLU activation, Batch Normalization, and multiple Dense layers. This robust approach surpasses existing models by managing both underfitting and overfitting, while delivering superior performance.
Authors - T. Sridevi, Chidhrapu Harini, Kurella Sai Veena Abstract - Sign Language is the primary means of communication among 1.8 million deaf people across India, and although Indian Sign Language (ISL) translation to technology-based effective solutions is still very limited, tremendous effort has so far been made in global research in sign language recognition. Nevertheless, the challenge persists in transcoding of text from ISL. This project will fill the vacuum by developing a deep-learning-based model capable of generating subtitles for ISL videos. With a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) for encoding the temporal pattern, the model learns on the Indian Sign Language Videos dataset. Designed in a manner to achieve high-accuracy captioning of ISL for reliable communication with the Indian deaf community. This will provide access to means of communication for millions of ISL users, but at the same time offers a critical communication tool meant to facilitate improvement in circles of education, social life, and professional circles in India.
Authors - Anudeep Arora, Vibha Soni, Lida Mariam George, Anil Kumar Gupta, Ranjeeta Kaur, Neha Arora, Neha Tomer, Prashant Vats Abstract - In the field of financial analytics, stock market prediction continues to be one of the most difficult and sought-after objectives. A key component of stock price modeling and forecasting is time series analysis, a statistical technique that examines sequences of data points gathered at successive times. A thorough review of time series analytic techniques for stock market prediction is given in this article. These techniques include machine learning and deep learning, as well as more sophisticated approaches like GARCH and ARIMA. It addresses the drawbacks and advantages of these methods, looks at the difficulties in putting them into practice, and identifies new developments in time series forecasting. Investors and analysts may improve their ability to anticipate the future and make better judgments in the ever-changing stock market environment by being aware of these techniques and how they are used.
Authors - M R Shreyaank, Dhanush Karthikeya A J, Dhanush Rajan S, Ashwini Bhat Abstract - This research attempts to investigate the potential healing effects of Vedic chants and music on the human brain through an in-depth analysis of EEG signals. The Vedic chants are known for their inherent calming and meditative attributes and are believed to impart positive influences on the human mind and body. The study employs a simulative model to analyse EEG signals during exposure to Vedic chants. Recorded EEG signals from MDD (major depressive disorder) subjects are subjected to preprocessing and feature extraction processes involving frequency-domain analysis and power spectral density. The study compares the extracted features between conditions of Vedic chant exposure and controlled settings and shows that there is significant increase in alpha and beta powers after listening to the specified chants. Rejuvinating and Calming chants showed the best positive impact.
