Authors - Nirav Bhatt, Purvi Prajapati, Nikita Bhatt, Jiten Bhalavat Abstract - A crucial stage in medical image analysis for brain tumor diagnosis, treatment planning, and patient monitoring is brain tumor segmentation. It entails locating the tumor and any of its subregions, including the necrotic core, peritumoral edema and an enlarging tumor. Manual segmentation takes a lot of time and is prone to mistakes, which makes it unsuitable for regular clinical use. Recently, deep learning-based techniques have shown promise as a method for automatically segmenting brain tumors. In this work, we suggest a deep learning method for automatically segmenting brain tumors from magnetic resonance imaging (MRI) scans using a UNet architecture. A deep learning architecture created especially for image segmentation is the U-Net model. It is composed of an encoder-decoder structure, where the decoder reconstructs the input image and the encoder extracts features from it. On a range of medical image segmentation tasks, including brain tumor segmentation, the U-Net model has demonstrated state-of-the-art performance.
Authors - Divya Lakshmi R, Jayasri R, Deepak Gupta, Shobhana Palat Madhavan Abstract - The rise of social media has significantly influenced consumption behaviour, with influencers shaping purchasing decisions across industries. However, a countermovement— de-influencing—has emerged, urging consumers to rethink their buying habits and embrace conscious consumption. This study explores how de-influencers impact consumer decision-making by discouraging excessive and unsustainable purchases. This study employs the theoretical framework of Consumer Resistance Theory. Through an online survey of 192 respondents, the research examines factors such as trust in de-influencers, follower congruence, ethical concerns, and message content in shaping anti-consumption tendencies. The findings show that de-influencers are linked to attitudes that support sustainable consumption. There is a positive relationship between follower alignment and brand avoidance, while ethical concerns are strongly tied to anti-consumption and conscious consumption. A minimalist mindset also aligns with these patterns, indicating that de-influencers influence attitudes that lead to more sustainable choices. The study offers valuable insights for marketers, policymakers, and sustainability advocates, shedding light on the growing digital influence landscape and its implications for sustainable conscious consumption practices.
Authors - Pranali G.Chavhan, Ritesh V. Patil Abstract - The race between the rapid spread of ubiquitous computing and the Internet of Behavior has opened up a whole new avenue for the provision of personalized and context-aware services. To this end, the work presents a proactive recommendation algorithm that is meant to capitalize on IoB data to predict user behavior and deliver tailor-made content in an unobtrusive manner. With real-time behavior information added to the mix, it intends to go a step further from traditional recommendation systems. What differentiates this approach is its use and manipulation of a multitude of contextual factors: geographical context, temporal context, context of what device is being used - perhaps even emotional context as well. This live blending affords the system the ability to respond to what is happening in the real world, thus making it more reactive and relevant. The simulation results show that this awareness of context is going to generate a significantly better user engagement and accuracy of recommendation rather than traditional systems.
Authors - D Kishore Babu, K Subba Rao, Nagesh Babu Dasari, Kumara Raja, Golla Mary Prakash Kumari, Vijayakumar Chilamkurthi Abstract - A neurological disorder identified as Parkinson's disease (PD) is described through a continuing loss of dopamine producing brain cells, which results in bradykinesia, rigidity, and tremors. Symptoms typically appear after 60 - 80% of these cells have disappeared. 7 - 10 thousand people suffer with Parkinson's disease throughout the world, primarily affecting those over fifty, while 4% of cases also affect younger age groups. 90% of Parkinson's disease patients experience speech issues early in the disease. Machine learning algorithms offer a practical means of diagnosing Parkinson's disease early on by analyzing speech features. By using voice datasets from the UCI Machine Learning Repository, these methods may effectively and with low error rates classify Parkinson's disease (PD). This raises the likelihood of an early diagnosis and course of action.
Authors - Manasi Sangamnerkar, Prachi Mukherji, Seema Rajput, Nandini Kendre, Vaishnavi Mudaliar Abstract - This paper gives a comparative study of the K-means clustering algorithm run on three platforms: a CPU, an FPGA, and a hybrid CPU-FPGA setup, focusing on execution efficiency and scalability. The CPU version is suitable for small datasets due to its simple serial processing ability, while the FPGA shows superior performance for larger datasets with hardware acceleration and parallel processing. The hybrid setup employs the ARM Cortex-A9 processor in addition to the programmable logic of the Xilinx ZedBoard (ZYNQ-7000 SoC). The algorithm is run through Vitis on the CPU, while AXI-interfaced IP cores, developed using Vivado, provide signal monitoring and real-time debugging through the Integrated Logic Analyzer (ILA). This setup provides dynamic software control and high-speed processing. The FPGA showed an execution time of 38.077 nanoseconds, compared to the 0.015519 seconds on the CPU, providing a speedup of about 106 times. Implementation issues, such as the lack of native floating-point support and reliance on fixed-point approximations, have been noted for future improvement. Additionally, 8-bit binary representations of centroids are visualized using LEDs on the FPGA, providing a physical and intuitive visualization of the clustering process. This paper illustrates the effectiveness of FPGAs and hybrid CPU-FPGA setups in accelerating compute-intensive machine learning algorithms and the benefits of hardware-based optimization in real-time and embedded systems.
Authors - Roshni De, Debatosh Chakraborty, Dwijen Rudrapal, Baby Bhattacharya Abstract - Floods are one of the most dangerous natural disasters, both frequent and dynamic due to continuous land use changes and climate change. This causes difficulty in predicting the areas most vulnerable due to their complex nature, causing heavy loss and damage. The study presents a data-driven framework for flood susceptibility mapping, examining the influence of multiple satellite-derived geo-spatial and temporal features in the Cachar district of Assam, India—a region frequently impacted by monsoonal flooding. By integrating Machine Learning with features derived from NDVI (Landsat 8), LULC (Sentinel-2), topographic variables (SRTM DEM), soil texture (OpenLandMap), and monsoon precipitation (CHIRPS), alongside flood extent information obtained from NDWI and Sentinel-1 SAR data, the model aims to enhance predictive accuracy in flood-prone, data-constrained environments. A rigorous feature selection process using IGR and VIF score and comparative evaluation across various classifiers was used to optimize the model. The study highlights the importance of integrating machine learning with remote sensing data to construct a precise flood risk model to aid the disaster management team in identifying vulnerable regions.
Authors - Shri Harini A, Niranjana Shaji, Karthick rajaa A S, Gugapriya G Abstract - Visual impairment increases fall risk, particularly among older adults with low vision facing a 16% higher likelihood of falls and those with blindness experiencing a 40% increased risk. To address this, the research presents an ML-driven, IoT-enabled smart cane equipped with sensor-based behavior analysis for real-time fall detection and mobility assistance. The system analyzes motion patterns and sudden orientation changes that helps in detecting falls while integrating obstacle detection with multi-modal feedback. Designed with low-power and cost-effective embedded components, the system ensures efficiency on resource-constrained devices, while IoT connectivity enables remote monitoring and caregiver communication. This smart cane offers a costeffective, scalable solution to improve independence and quality of life for visually impaired individuals.
Authors - Subhashree Banerjee, Ranit Roy, Anirban Chattopadhyay Abstract - Having utilized the latest scientific and knowledge developments, Wireless-Sensing node Technology has made many strides in healthcare today. Many people, however, are made to suffer from different health-related issues and even death due to several illnesses, often from an absence of proper medical attention. There is an urgent need for an efficient, modern real-time patient monitoring system realized through the power of IoT. Continuous real-time tracking of vital health parameters, like temperature, blood pressure, oxygen saturation measurement, and electrocardiogram (ECG), and displaying their values other than ECG on LCD, along with alerts issued in case of deviations or abnormality for the reported health factor to the concerned healthcare professional, and storing the real-time stats of the patient in graphical form, makes up the proposed innovative system. Incorporated with different specialized sensors, including the temperature sensor, blood pressure sensor, SPO2 sensor, and ECG sensor, and also providing two microcontrollers accompanied by software, the system is intended to meet the primary purpose of establishing a robust patient management infrastructure rooted in IoT. By adopting this high-tech system, the health workers will be better placed to monitor their patients anywhere they might be, either in a hospital setup or even at the convenience of their homes, through an integrated IoT-enabled healthcare platform. The overarching goal of this initiative is to guarantee the delivery of top-tier patient care services while promoting enhanced health outcomes and overall well-being for individuals under medical supervision.
Authors - Jyotsna More, Suvarna Aranjo, Martina D’souza, Siddhi Awlegaonkar, Saahil Chaurasia, Aditya Ghadge, Shreya Jadhav Abstract - Traditional voting systems suffer from several challenges, including voter impersonation, ballot tampering, multiple voting, and lack of transparency, which compromise electoral integrity. To address these issues, this paper proposes a Blockchain-Based Biometric Voting System that integrates fingerprint authentication with blockchain technology to enhance the security, transparency, and reliability of elections. Biometric authentication ensures that only registered voters and administrators can access the system, eliminating impersonation and fraudulent voting. The R307 fingerprint module is used for real-time voter authentication, preventing unauthorized access. Once verified, votes are recorded on a blockchain ledger, ensuring data immutability and decentralization. Unlike conventional databases, blockchain technology eliminates single points of failure, preventing vote manipulation and unauthorized modifications. The system also incorporates a web-based interface that allows voters to register, authenticate, and securely cast their votes, while administrators can manage the election process with full transparency. Blockchain’s decentralized nature ensures that all transactions, including candidate registration, vote counting, and election results, are securely stored and verifiable, preventing tampering and external interference. Initial testing demonstrates high accuracy in biometric verification and efficient blockchain-based vote storage, making the system scalable for local, state, and national elections. By integrating biometric security with blockchain's trustless nature, this system provides a fraud-resistant, transparent, and tamper-proof voting solution, fostering greater public confidence in electoral processes.
Authors - Mathesh H P, Shanthini E, Praveen S, Praneshwaran M S Abstract - Environment with green practices depends heavily on efficient waste management, especially in parts of the economy where garbage is produced and processed in huge quantity Plastic bottles are one of the largest impediments to waste material due to their bulk use and destructive effect on the environment Inability of conventional methods of garbage segregation to offer the required precision and speed to function in most situations renders garbage segregation ineffective. To counter the issue, in this project, an improved waste sorting mechanism using the YOLOv8 object detection algorithm is utilized. Image processing and real-time machine learning are utilized by the system to separate and filter plastic bottles from the rest of the waste materials on a conveyor belt accurately. On grounds of efficiency and effectiveness, the YOLOv8 algorithm is superior to traditional methods and previous models. It is also highly renowned for detecting objects with a very high degree of accuracy. Other methods did not extend beyond detection, but auto-sorting ensures that plastic trash is sorted according to what needs to be recycled. This raises the overall processes of waste management and lowers contamination. Because it can provide businesses with a more and more scalable option, this invention is a giant leap for automated waste management.
