Authors - Hemlata V. Gaikwad, Sushma S. Kulkarni Abstract - The present article explores the dynamic changes in required graduate attributes to develop industrial revolution 4.0 (IR4) ready. The assurance of graduate attributes assumes more importance given the fact that only 20% of the engineers are employable for any job in the knowledge economy (NER, 2019). Indian Engineering institutes across India are facing pressure and striving hard to equip the graduates with the right set of attributes to enhance their employability. The authors examine the shifts of graduate attributes from industrial revolution I to IV to perceive the most important ones from employment perspective. Second, we recommend the strategies at multiple levels to develop the identified attributes by mapping them with program educational objectives and finally we argue that all these strategies must be aligned to strengthen outcomes based education and as a result prepare employable and environmentally sensitive graduates responsible for a sustainable future..
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma Abstract - IoT has brought significant changes in different aspect of the society, social-relational and economic life and how society interacts with environment and physical space. Starting from light switches, locks that open doors, self-driving cars, various bracelets that track a person’s health state, sensors in industries that control machines, the Internet of things brought us to the world of increased connectivity that is filled with various conveniences, effectiveness, and automation. However, this link has given a new level of added insecurity since this makes it easier for cybercriminals to get into systems, manipulate important services, and steal important information. Consequently, this research paper provides the analysis of the complex nature of IoT security and identifies the primary threats inherent in IoT devices and networks. Comparing these models, the outcome is also presented, according to which these vulnerabilities can lead to an organized attack on data and personal accounts that can encompass the theft of personal information, attack on organizational accounts, and disruption of infrastructure and utilities. Moreover, the paper presents material considering IoT security issues as complex would be an understatement, one having to do with its technical, non-standards, and dynamic nature. Analyzing such vulnerabilities and risks, this paper intends to clarify the significance of sound security measures and protective actions that must be put in place to reduce possible threats in the IoT sphere.
Authors - Rohini Hongal, Rahil Sanadi, Supriya Katwe, Rajeshwari .M Abstract - Image compression involves reducing digital image file sizes while keeping their quality intact. Interval arithmetic, a mathematical technique, deals with ranges of values rather than precise numbers, allowing for robust computations across various applications. Vector quantization is a data compression method that groups similar data points to represent data efficiently. This study attempts to create an advanced image reduction method by integrating Convolutional Neural Networks (CNNs) and Interval-Arithmetic Vector Quantization (IAVQ). The study also examines and validates the practical relevance of attribute preservation. In the compression stage, the trained CNN is employed to extract features from input images, and the interval-arithmetic-based quantization maps these features to the predefined quantization intervals, considering attributes like sum, difference, and product. The proposed framework involves two main stages: training and compression. During the training stage, a CNN is trained to learn feature representations that encapsulate important image characteristics such as contrast and luminance intensity. This project thoroughly examines Normal Compression and Interval Arithmetic Compression, with the latter displaying promising results. Notably, Interval Arithmetic Compression consistently yields superior outcomes in pixels per second, compression file size, and PSNR compared to normal compression techniques. The IAVQ method achieves nearly 20 per cent higher compression quality, reduces pixel count by 0.5 to 0.9 per second, lowers PSNR ratio by 0.7 to 2.3 dB, and saves 11 to 19 KB of storage compared to the standard method across all image types.”
Authors - Harsh Murjani, Kabir Mota, Nishat Shaikh Abstract - This research paper explores the critical importance of Open Source Intelligence (OSINT) in modern law enforcement practices. The study aims to elucidate the essential role of OSINT tools and methodologies in enhancing the capabilities of law enforcement agencies to gather, analyze, and utilize intelligence from publicly available sources. The findings underscore the significant impact of OSINT on various aspects of law enforcement operations, including proactive threat detection, investigative support, and strategic decision-making. Moreover, the study identifies key benefits of OSINT, such as its cost-effectiveness, scalability, and ability to provide timely and actionable intelligence.
Authors - Abdul Razzak R Yergatti, Prajwal Shiggavi, Mohammed Azharuddin, Suneeta V Boodihal Abstract - Traditional defense solutions like intrusion detection and thorough packet inspection are not so accurate. These techniques include signature-based detection, which uses known patterns, and heuristic or behavioral analysis, which evaluates program behavior to detect suspect activities. The demand for more advanced and continuously innovative methods to combat malware, botnets, and other malicious activities is urgent. Machine Learning (ML) emerged as a promising approach due to increasing computing power and reduced costs, offering potential as either an alternative or complementary defense mechanism to enhance detection accuracy by learning from large datasets of known malware behaviors. This investigation delves into the capability of Machine Learning in detecting malicious malwares within a network. Initially, a thorough analysis of the Netflow datasets is conducted, resulting in the extraction of 22 distinct characteristics. Subsequently, a feature selection procedure is employed to compare all these characteristics against each other. Following this, five machine learning algorithms are assessed using a NetFlow dataset that encompasses typical botnets. The outcomes reveal that the Random Forest Classifier successfully identifies over 95% of the botnets in 8 out of the 13 scenarios, with detection rates exceeding 55% in the most challenging datasets.
Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade Abstract - An essential concern of our community is enhancing education. Everyone would wish to see a smaller class and school, but technology cannot materialize it physically. For the instructor, though, technology may function as a "force multiplier." For the guidance of researchers, a comprehensive questionnaire is formulated for pertinent data from the primary source, and a survey is conducted in the targeted area for determining influence. Using the questionnaire, in-depth conversations were carried out with certain main data sources toward gaining an understanding of the perspectives, mindsets, and behaviors. This would give the researchers any kind of recommendation they may deem necessary and helpful. Statistical tools such as tabulations, grouping, percentages, averages, hypothesis testing, etc. are applied to process the questionnaire. The following are considered when it comes to streams: arts, science, commerce, engineering, medicine. Because technology is always changing, even though we update these pages often, we cannot promise that all the information will remain current. Please visit our technology-focused top page to view the most recent and pertinent tech headlines. In the information age, we can now communicate with one another in ways that were before unthinkable. Educators and administrators are facing a new problem as they try to figure out. Technology has advantages. Using web conferencing or other tools, parents and teachers may be able to work together virtually. This holds true whether they use the internet for virtual communication with professionals or fellow students, or for research purposes. These programs also impart to students the technology skills needed to succeed in the modern workforce.
Authors - More Swami Das, N.N.S.S.S.Adithya, Gunupudi Rajesh Kumar, R. P. Ram Kumar Abstract - In image processing and computer vision, human activity detection is a significant activity. There are various techniques and approaches for key point detection that identify the external Skelton key points. Some methods will detect key points and recognise the human pose. The proposed work aims to utilise the Random Forest (RF) approach and classify the human activity into 15 classes using media pipes. The library trained with 30,000 samples. The objective of this paper is to capture the human face, like the angles of limbs and key points, and train the machine learning model to recognize the human action using media pipe. In the future, we can extend this work to capture real-time video poses using intelligent methods for key points to identify the actions of human facial expressions.
Authors - Muskan Dave, Mrugendrasinh Rahevar, Arpita Shah Abstract - These days, bone breaks are an all-over issue that can be invited on by a couple of special events, similar to lamentable lifestyle decisions and car collisions. The human body's ability to move and expect different shapes depends upon bones. This can cause serious misery, developing, and inconvenience moving the influenced appendage. X-radiates are a useful and sensible technique for finding breaks. For the patient, missing a break has serious outcomes. The revelation of bone breaks using CNNs ought to be conceivable using a couple of unmistakable estimations. This paper proposes a couple of regularly used computations incorporate move learning estimation using a pre-arranged CNN model, as VGG. The proposed method includes optimized time, resources and different CNN models. Multi-view CNN estimation uses various X-pillar viewpoints on comparative bone, similar to front back and equal points of view, to chip away at the precision of break revelation. Hybrid CNN computation joins various CNN models, similar to a 2D CNN and 3D CNN, to deal with the precision of break disclosure. Existing systems are being investigated and developed ceaselessly, and it similarly depends upon the dataset open, the kind of imaging and the essential of the use case. Significant learning uses additional mystery layers of the ANN that rely upon mind associations. This paper summarise significant learning approaches for separating bone breaks.
Authors - More Swami Das, Gunupudi Rajesh Kumar, R. P. Ram Kumar Abstract - Cloud-Computing enables ubiquitous on demand, convenient network access to a shared pool of computing resources. Cloud services are provided by organizations that manage huge data. The problem is to provide cloud security and availability of services to all authenticated users. In this work, We use Cloud Security Model (i.e. Encryption and description of cloud data) to enhance security and also increase availability through the use of virtualization technologies like Hyper-V and efficient utilization of cloud services. In the future, we can extend this architecture to prevent hacking and trusting models.
Authors - Vanishree Pabalkar, Ruby Chanda, Debjyoti Abstract - When it comes to n recent manufacturing, lines of Production and the Work that is in process are considered as the crucial aspects that determine the Production Performance. TAKT is Takzeit, which means Rhythm of Music. TAKT is a defined as the tool that measures the methods of Production. TAKT times is nothing but the complete time within the defined range. The current study explains the way in which technologically advanced cutting tools can be used for cycle time reduction. The impact of these advanced cutting tools on Production Performance is studied here. The objective is to increase productivity of a particular aspect. This is done by assessing the challenges that occur in the Production process. The ways to do away with the challenges that create obstacles are also identified. The Action taken to enhance Production is discussed. The existing mapping that is termed as the value stream mapping has been considered to explain the current situation of Production and provide suitable solutions. Technologically advanced cutting tools reduce the cycle time and reduce the cost in the Production process.
Authors - Smita Mehendale, Reena (Mahapatra) Lenka Abstract - This system is provided to make healthcare services responsive to visually impaired patients’ needs in various circumstances, predominantly during a medical emergency. The system includes multiple stakeholders, including visually impaired patients, patient family members, friends, neighbors, hospitals, healthcare providers, insurance companies & agents, private medical attendants & agencies, pharmacies, blood banks, and medical equipment providers using voice-assisted AI and with the help of various proposed systems. The design and implementation of voice-assisted personalized, comprehensive medical service, both emergency and non-emergency, will use data from shared information by patients and healthcare service providers like hospitals, pharmacies, and pathology and allied services like insurance and medical attendant services. The system uses a voice assistant chatbot to communicate with patients and a user interface with medical and allied service providers. It communicates between multiple service providers, clearly showing the entire patient care cycle for the visually impaired, a special group of people.
Authors - Jay Bhatt, Bimal Patel, Anshuman Prajapati, Jalpesh Vasa Abstract - Software engineering has progressed extensively, adopting structured methodologies and systematic frameworks for developing reliable, scalable, and efficient systems. A key advancement has been the introduction of software process models, which guide development activities. Traditional models use linear, sequential phases, but are rigid and less suited for projects with changing requirements. To address dynamic market demands and evolving business needs, the Agile methodology emerged, providing an iterative, flexible approach to software development. Agile promotes incremental delivery, collaborative team dynamics, and continuous customer feedback, making it highly effective in rapidly changing environments. Agile methodologies have expanded beyond software development into industries like media. With fast-evolving technology and shifting audience behaviors, media companies are under pressure to innovate. BBC News adopted Agile to overhaul its content production and delivery processes. This transition has improved newsroom agility, enabling faster response times, fostering cross-functional collaboration, and enhancing iteration capabilities in digital media workflows. The shift to Agile represents a strategic transformation, positioning BBC News to adapt to audience demands and technological advancements. This paper investigates the integration of Agile at BBC News, detailing the operational benefits, challenges, and the methodology’s influence on sustaining their leadership in the competitive news industry.
Authors - Ankit Aal, Priyanka Patel Abstract - In today’s interconnected world, ignoring ethical decision-making can have dire consequences. As businesses expand and globalize, the pressure to cut corners and maximize profits can lead to severe ethical breaches. William C. Butcher, retired chairman of the Chase Manhattan Corporation, highlighted the growing recognition that ethics in business is not a luxury but a necessity. Rooted in the concept of “ethos,” the importance of ethics has evolved, especially as business practices have become more complex. Over the decades, unethical business behavior has left a significant mark: the 1960s were defined by social upheaval, the 1980s by rampant financial scandals, and the 1990s by the challenges of a newly globalized economy. However, the rapid growth of markets was paralleled by troubling issues such as the exploitation of child labor, environmental degradation, and product counterfeiting. The 21st century introduced even more sophisticated threats—cybercrimes, intellectual property theft, and workplace discrimination—placing companies at greater risk if they neglected ethical practices. Despite the increasing awareness of these challenges, many businesses still struggle to balance profit with principle. Those that fail to integrate ethics into their strategies risk damaging their reputation, alienating customers, and facing legal repercussions. On the other hand, companies that proactively embrace ethical standards benefit from increased trust, a loyal workforce, and sustainable profitability. As ethics become an integral part of strategic business planning, they act not only as a safeguard against malpractice but also as a catalyst for long-term success in the global marketplace.
Authors - Samrat Subodh Thorat, Dinesh Vitthalrao Rojatkar, Prashant R Deshmukh Abstract - Vehicular Adhoc Networks (VANETs) play a vital role in enhancing road safety, and traffic management, and providing infotainment services. Various protocols have been developed to facilitate communication in VANETs, each with its advantages and limitations. This paper shows a comparative analysis of different VANET protocols, mentioning their performance, scalability, and reliability. It also illustrates the need for a hybrid protocol using satellite communication and GPS networks to overcome existing issues and challenges thus improving overall system efficiency.
Authors - Ruby Chanda, Rahul Dhaigude Abstract - There is a need to map, analyse, and visually convey a product's environmental impact over its complete life cycle or a specific aspect of it, given the urgency with which climate change must be addressed. If "what-if" scenarios can be additionally supported this can accelerate decision making towards improved environmental outcomes. Practically such outcomes need to also understand the economic ramifications so the map needs to support a mix of environmental and operational efficiency metrics. This paper explores the adaptation of a leading commercial value stream mapping software (eVSM Mix) for this purpose. Value stream maps come from the Lean domain and provide a high level view of the activities required to provide customer value. The work involved has technical and marketing aspects tied to a new product introduction and to a new customer segment.
Authors - Masakona Wavhothe, Khutso Lebea Abstract - This paper focuses on the vulnerabilities present in Bluetooth Low Energy (BLE) Beacons by exploring the background of BLE technology and the need to explore the chosen topic. The problem statement and structure of the paper are also explored in the introductory section. The subsequent section covers the case study that will be used to explore the chosen topic in deeper detail. Then, the background explores BLE beacons in detail, explaining their applications and vulnerabilities. The paper then concludes by highlighting all the important facts established in the research and suggesting how the study can be improved.
Authors - Mahek Viradiya, Shivam Patel, Sansriti Ishwar, Veer Parmar, Simran Kachchhi, Utsavi Patel, Hardikkumar Jayswal, Axat Patel Abstract - Moisture content identification in soil is crucial for various applications in agriculture, construction, and environmental monitoring. Traditional methods for moisture detection often involve labor-intensive processes and specialized equipment which can be invasive, time-consuming, and expensive. This study explores use of spectrometry data, acquired through multispectral sensors using visible light and near-infrared (NIR) spectrum ranging from 400-1000nm, for rapid and accurate moisture identification in soil and sand samples. The sensors leverage on-chip filtering to integrate up to eight wavelength selective photodiodes into a compact 9x9mm array, facilitating the development of simpler and smaller optical devices. The neural network model compromises of input layer, one hidden layer, and an output layer, developed using Tensor-flow and Keras libraries. It was trained using the Adam optimizer and sparse categorical cross-entropy loss function for 35 epochs with a batch size of 16. Results indicate that the neural network model and appropriate classifiers can successfully classify soil moisture levels into 4 distinct categories based on given dataset, demonstrating its potential as a cost-effective and efficient alternative to traditional soil moisture measurement techniques.
Authors - Ruby Chanda, Reena Lenka Abstract - E-games and gamification stand out among the innovative pedagogical techniques brought out by the swift integration of digital technology in educational settings because of their capacity to revolutionize the learning process. In order to outline the development, present trends, and future research directions on e-games and gamification in education, this study uses a predictive bibliometric analysis. Using an extensive dataset of Scopus publications from large academic databases, we use cutting-edge bibliometric methods to pinpoint important research themes, significant figures, and foundational works in this emerging subject. Our study displays that, particularly in the previous ten years, there has been a perceptible upsurge in scholarly interest in and publications about e-games and gamification, which is indicative of the rising understanding of these technologies' capacity to engage and encourage students. The study shows that this research area is multidisciplinary, with notable contributions from computer science, psychology, educational technology, and game design. The design and execution of educational games, the psychological principles behind gamified learning environments, and the effectiveness of gamification in improving learning outcomes are among the key study areas that have been highlighted. Our study offers useful insights for academics, educators, and policymakers looking to maximize the educational potential of e-games and gamification by identifying existing tendencies and predicting future developments. In addition to highlighting the current status of research, this predictive bibliometric study also lays out a roadmap for future studies and applications in the digital frontier of education.
Authors - Ruby Chanda, Vanishree Pabalkar Abstract - The revolutionary potential of artificial intelligence (AI) to improve agricultural practices and outcomes in India is examined in this research. India, a country that depends mostly on agriculture, has many difficulties, such as erratic weather patterns, pest infestations, and ineffective resource management. Artificial intelligence (AI) technologies provide creative answers to these issues, including machine learning, predictive analytics, and IoT-enabled gadgets. AI has the capacity to analyse enormous volumes of data and deliver timely insights and practical recommendations to farmers, resulting in increased agricultural yields, more efficient use of resources, and sustainable farming methods. This paper looks at the use of AI in Indian agriculture today, namely in the areas of automated irrigation systems, insect detection, and precision farming. It also covers the socioeconomic effects of AI adoption, emphasising how farmers could benefit from higher productivity and profits. In order to fully realise the benefits of artificial intelligence (AI), the study finishes with an analysis of the opportunities and obstacles related to its deployment in the Indian agriculture industry. It emphasises the necessity for supportive policies and infrastructure.
Authors - Ruby Chanda, Reena Lenka Abstract - This invention utilized image processing to achieve the MCQ revision in an extremely simple way. It creates extraordinary work to arrange to eliminate the boundaries of multi-decision evaluation remedies. We are here utilizing the Open-Source PC Vision Library (Open CV) to process and address the responses. The utilization of Numerous Decision Questions (MCQs) to test the information on an individual has been expanded progressively. These tests can be assessed either utilizing OMR innovation or physically. Continuously, it is very challenging to have an OMR machine under all conditions, and simultaneously, manual adjustment is tedious and mistake-prone. These disservices have been conquered in our proposed framework by utilizing an advanced picture-handling strategy to address the responses on the OMR sheet.
Authors - Ravi Tene, Dasari Kalyani, N. Sudhakar Yadav, Kondabala Renuka, Gunupudi Rajesh Kumar, Nimmala Mangathayaru Abstract - A deepfake is a misleading video or image that looks genuine. GANs (Generative Adversarial Networks) are the known name in the domain of machine learning. GANs generate a huge amount of fake human writing with deep-learning-wide-models. The generator model learns to sample points from a latent space so that new samples of the same distribution can be fed in and produce different observable model outputs. Deepfakes for most applications can be convincingly created using Generative Adversarial Networks (GANs). There are fears on the Internet related to deepfake. However, the authors use ResneXt and LSTMs for using Deep Learning Network to identify fake areas of deepfake uses Python facial recognition and C++ visual libraries to identify a face in this video. Fake videos are further validated using models trained on various edge groups.
Authors - Vanishree Pabalkar, Rahul Dhaigude Abstract - The purpose to carry out the research study is to understand the concepts of introducing advanced technology that prevails in EV market and the challenges and strategic solutions. This research validates customer feedback and allows companies to get closer to the true opinion of potential Indian customers. In addition, this can eliminate misunderstandings and problems to trade better. This study was conducted to understand the factors that influence the choice of an electric vehicle. The current research has been conducted to study the purchasing behavior of consumers when purchasing electric cars by identifying the importance ratings assigned to different factors during the selection process. in the electric car, and analyze the reasons for the brand's success by identifying the levels of excellence. Current users use different types. Characteristics and identification of the gap between importance ratings and current ratings.
Authors - Vatsal Suchak, Harmin Rana, Ayush Verma, Nilesh Dubey, Hardikkumar Jayswal, Dipika Damodar, Chirag Patel Abstract - Cattle farming plays a crucial role in global food production, but monitoring the health of large herds poses significant challenges. Traditional manual inspections are inefficient, reactive, and prone to error, highlighting the need for scalable, automated health monitoring systems. This paper introduces a smart cattle health monitoring system that utilize the Internet of Things technology and machine learning algorithms to provide real-time health tracking. The system used a proposed wearable devices equipped with ESP32 microcontrollers and sensors to monitor cattle’s vital parameters, such as body temperature and heart rate. Data collected from the devices is transmitted to a local XAMPP server and analysed by an edge-computing device, Jetson Nano, which processes the data using supervised and unsupervised machine learning models for anomaly detection. If health anomalies are detected, the system sends real-time alerts to farmers, allowing for timely intervention. The system’s design focuses on local processing for low-latency performance, scalability for large herds, and robust security measures. This project demonstrates the potential of IoT-based livestock health monitoring systems to enhance productivity, improve animal welfare, and reduce economic losses due to illness.
Authors - Teena Bambal, Dipesh Chavan, Nikhil Gadiwadd, Deepak M. Shinde Abstract - This survey paper investigates the application of artificial intelligence (AI) and machine learning (ML) techniques for the early detection and diagnosis of liver disease. Traditional methods of liver disease diagnosis, such as blood tests and imaging techniques, can be time-consuming and prone to human error. AI-based approaches offer the potential to improve accuracy, efficiency, and accessibility of liver disease diagnosis. The research investigates a range of AI and ML algorithms, such as decision trees, support vector machines, random forests, neural networks, and deep learning models. These algorithms are applied to analyze large datasets containing patient information and medical test results. The performance of the models is evaluated using metrics such as F1-score, precision, accuracy, recall, and AUC. The findings demonstrate the effectiveness of AI-based approaches in accurately detecting liver disease. Compared to traditional methods, AI models can provide more reliable and timely diagnoses, leading to improved patient outcomes. The research highlights the potential of AI to revolutionize the field of liver disease management and improve global healthcare.
Authors - Yatin Nargotra, Tanya Jagavkar, Tushar Birajdar, L.P.Patil Abstract - The Mahavitaran Help App is a mobile application aimed at revolutionizing the process of reporting electrical outages in India. Current systems for outage reporting are often slow, inefficient, and lack the integration needed to quickly address user complaints. The Mahavitaran Help App simplifies this process by allowing users to submit complaints via mobile devices, integrating location services with Google Maps and supporting the upload of complaint-relevant images. Moreover, this project introduces a critical migration from Firebase to AWS or Google Cloud, offering improved scalability, reliability, and faster processing of complaints. This paper presents a detailed review of existing mobile complaint management systems and explores cloud-based scalability and security features, including OTP authentication for securing user data.
Authors - Rani S. Lande, Amol P. Bhagat, Priti A. Khodke Abstract - Visual memes have become a pervasive form of communication in digital spaces, presenting a unique challenge and opportunity for content analysis due to their blend of visual, textual, and often humorous elements. This paper reviews and synthesizes methodologies employed in the analysis of visual memes, aiming to provide a comprehensive overview of current practices and future directions. The methodologies discussed encompass a range of approaches, including qualitative, quantitative, and mixed-methods strategies. Qualitative methods delve into semiotic analysis, exploring how visual and textual components interact to convey meaning and cultural references. Quantitative approaches employ computational tools to analyze large datasets, focusing on metrics such as image recognition, sentiment analysis, and virality metrics. Mixed-methods studies combine these approaches to offer nuanced insights into the multifaceted nature of visual memes. Challenges in visual memes content analysis include the rapid evolution of meme formats, cultural context sensitivity, and the ethical implications of meme reuse and modification. Additionally, the paper explores emerging trends such as deep learning techniques for image recognition and natural language processing for text analysis within memes. By synthesizing these methodologies, this paper aims to provide researchers and practitioners with a foundational understanding of how to effectively analyze visual memes, highlighting opportunities for interdisciplinary research and applications in fields ranging from communication studies to digital humanities and beyond.
Authors - Nitika Sharma, Rohan Patel, Hardikkumar Jayswal, Nilesh Dubey, Hasti Vakani, Mithil Mistry, Dipika Damodar, Shital Sharma Abstract - This study explores the use of advanced machine learning models to forecast trends in Apple’s stock market performance. Stock market forecasting presents a formidable challenge, given the inherent volatility and unpredictability of market behavior. The study investigates various advanced models, such as Logistic Regression, XGBoost, Artificial Neural Networks, Recurrent Neural Networks, Long Short-Term Memory (LSTM), and ARIMA, for predicting stock prices. Analyzing historical data spanning from 2014 to 2024, which includes Apple's daily stock prices and trading volume metrics, the research applies Grid Search optimization to fine-tune model parameters, thus enhancing predictive accuracy. The findings reveal that LSTM achieved the highest accuracy at 96.50%, followed closely by ARIMA at 90.91%. These results highlight the critical role of machine learning in improving stock price predictions, thereby facilitating more informed investment decisions.
Authors - Anant Nikam, Atharva Gangapure, Samarth Deshpande, Sonali Shinkar Abstract - Among the primary concerns in the digital era, secure sharing of data stands prominent. The integrity, confidence, and authenticity of information being shared form a very significant concern. Blockchain technology promises much towards overcoming such challenges due to its decentralized and immutable nature. It significantly enhances data security through its use of encryption techniques, such as hashing and digital signatures, which eliminate the need for middlemen in transactions while also reducing the probability of data breaches. It leverages smart contracts that provide mechanisms for automating access controls wherein data is shared appropriately, according to agreed terms, among the participants in a trustless environment. Some practical illustrations of use cases from healthcare, supply chain management, and finance are found in the context provided below. Findings thus reveal the needed innovation to revolutionize the state of secure data sharing on blockchain technology by providing a strengthened, decentralized infrastructure that promotes trust, transparency, and accountability of stakeholders involved.
Authors - Dibyendu Rath, Arunangshu Giri, Dipanwita Chakrabarty, Puja Tiwari, Satakshi Chatterjee, Shamba Chatterjee Abstract - The study reveals how mobile health apps and information technology can take a pivotal role for healthcare improvement, especially for rural population, where people suffer from medical infrastructural inadequacy. Healthcare apps facilitate the users by providing a 24x7 accessibility at a cost-effective rate. The study used cross-sectional surveys for analyzing responses across different demographic profile, like age, gender, qualification, income group etc. This study has identified some key factors that help to engage customers with healthcare apps. The study also reveals that trust on healthcare app will enhance intention to adopt healthcare apps and trust will be positively influenced by Perceived Benefits (PB). Again, trust will be negatively induced by Perceived Risks (PR) and Technology Anxiety (TA). Four hypotheses were made to validate the relationships among the factors. Finally, the study balances the benefits and risks of using healthcare apps and guides how m-health technology can increase adoption intentions.
Authors - Vasu Agrawal, Nupur Chaudhari, Tanisha Bharadiya, Manisha Sagade Abstract - The recent progress in AI and deep learning has significantly transformed the public safety landscape, particularly in the area of real-time threat detection in public domains. This comes with increased complexity and density as urban environments become more complex; traditional surveillance systems are no longer enough for monitoring large crowds, detecting potential threats, or ensuring public safety. This has necessitated the development of automated systems that could process large volumes in real time to pick anomalies, suspicious behaviors, and objects liable to imperil security. We delve into the core methodologies that object detection models, such as YOLO, Faster R-CNN, SSD, and compare them .To further improve the accuracy in detection of anomalous and illegal activities and reduce false positives and negatives we created a custom dataset by fusing data from different sources, these systems enhance the overall reliability of the surveillance systems.
