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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Natarajan, Suresh Kumar Ghantasala, G S Pradeep Anthoniraj, S. Vidyullatha, Pellakuri Rajesh Sharma, R. Ananthanagu, U. School of Computer Science and Engineering Ramanagara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India

ISBN: (纸本)9798350385199

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model's ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet's potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model's proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model's classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively. © 2024 IEEE.

关键词： Medical imaging

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Exploring Integration of Multimodal Deep Learning Approaches for Enhanced Alzheimer's Disease Diagnosis: A Review of Recent Literature

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SN computer science 2024年第7期5卷 852页

作者： Deshpande, Sonali Kulkarni, Nilima Computer Science & amp Engineering Department MIT SoC MIT Art Design and Technology University Pune India

Alzheimer's disease (AD), is the most common form of dementia that affects the nervous system. In the past few years, non-invasive early AD diagnosis has become more popular as a way to improve patient care and treatment results. Imaging methods, electroencephalogram (EEG) tests, and sound evaluations are some of the new ways that researchers have looked into. This review covers 60 papers published from 2020. They are compared in terms of how they use basic deep learning models such as CNN, LSTM, Alex Net, Inception Net, VGG19, and ResNet to identify AD. But not many studies use more than one method together, like image and EEG, EEG and sounds, or images and sounds. The information from the Scopus database makes it easy to look at the newest information and work. This means that using more than one method to find AD isn't getting as much attention. Our review says that combining the best parts of each method in a mixed way could make Alzheimer's research much more useful and lead to better ways to diagnose. The paper talks about problems and opportunities in the field right now as well as possible study topics and issues for the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer's disease (AD) Deep learning Electroencephalography Machine learning Magnetic resonance imaging Neurological disorders Sound

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A Comprehensive Evaluation Process by Analytical Hierarchical Process for Identification of Potential Groundwater Zones 2

A Comprehensive Evaluation Process by Analytical Hierarchica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gudadhe, Amit Anil Reddy, K.T.V. Faculty of Engineering and Technology Computer Science and Design Department Wardha India Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798331528713

For social and economic development of any region, groundwater plays a very vital role. Surface water infiltration depends on various parameters of the earth. The parameters includes Slope, Geology, Soil Type, Land Use/Land Cover, Rainfall, Drainage Density based on the study area under consideration. There exists various techniques for recognizing of groundwater probable regions. This paper focuses specifically on the recognizing of the subsurface water probable regions by Analytical Hierarchy Process. The steps involved in the process are discussed in detailed and shows how the potential zone identification is based on value of weights evaluated. © 2024 IEEE.

关键词： Land use

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Residual Network (ResNet) Based Deep Learning Method for Detection and Classification of Accidents in Surveillance Scenes

Residual Network (ResNet) Based Deep Learning Method for Det...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Rahul Chiranjeevi, V. Sebastian, Livia Mary Manjusree, M. Senthil Pandi, S. Computer Science and Engineering Rajalakshmi Engineering College Chennai602105 India Computer Science and Design Rajalakshmi Engineering College Chennai602105 India

ISBN: (纸本)9798350305708

Road accidents have become a major cause of death worldwide. Common causes of these accidents include speeding, driving while intoxicated, and unpredictable weather conditions. However, the primary cause of these accidents is the inability to detect them in a timely manner. Although human operators are being used to monitor the surveillance continuous monitoring is difficult and to errors. Recently computer vision techniques are being deployed to detect the accidents automatically in surveillance videos. Many techniques have been developed in the field of computer vision and machine learning to detect the anomalies in the videos. However, most of the methods do not focus on detecting accidents and the state of art accuracy of the proposed models is very less. To overcome these issues a novel method is proposed using Residual Networks (ResNet) to detect the accidents in real time. Proposed ResNet architecture extracts the higher-level features from the input frames and recurrent network extract the temporal representations. Experiments have been performed on various accident detection datasets like HID12 and UCF CRIME. This study clearly distinguished between normal and abnormal behavior, demonstrating that ResNet is able to classify each accident into the appropriate group. ResNet50, ResNet101 and ResNet152 obtained 98.34% accuracy on the UCF-Crime dataset. © 2023 IEEE.

关键词： Anomaly Resnet Surveillance Video

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Exploiting Human-AI Dependence for Learning to Defer 41

Exploiting Human-AI Dependence for Learning to Defer

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Zixi Cao, Yuzhou Feng, Lei College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore Information Systems Technology Design Pillar Singapore University of Technology and Design Singapore

The learning to defer (L2D) framework allows models to defer their decisions to human experts. For L2D, the Bayes optimality is the basic requirement of theoretical guarantees for the design of consistent surrogate loss functions, which requires the minimizer (i.e., learned classifier) by the surrogate loss to be the Bayes optimality. However, we find that the original form of Bayes optimality fails to consider the dependence between the model and the expert, and such a dependence could be further exploited to design a better consistent loss for L2D. In this paper, we provide a new formulation for the Bayes optimality called dependent Bayes optimality, which reveals the dependence pattern in determining whether to defer. Based on the dependent Bayes optimality, we further present a deferral principle for L2D. Following the guidance of the deferral principle, we propose a novel consistent surrogate loss. Comprehensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our proposed method. Copyright 2024 by the author(s)

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Autonomous solar car 4th

Autonomous solar car

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kulkarni, Nilima Sayyed, Arshadali Soni, Nikhil Pal, Komal Singh, Prashant Computer Science & Engineering Department MIT SoC MIT Art Design and Technology University Pune India

ISBN: (纸本)9781032911571

Throughout the years, automobile companies achieved outstanding progress in manufacturing safe, reliable, and affordable vehicles. Many companies and manufacturers are developing autonomous cars for the future years. However, to achieve this goal they are facing too many challenges in technical and non-technical fields like software complexity, mapping, real-time data analysis, testing, and verification. AI Software installation in cars is costly, and also it affects fuel consumption. So, proposed an autonomous solar car that will run on solar energy, which is available free of cost. NumPy, OpenCV computer vision library, AI, ML, and Python were used for the implementation of the work. The edge detection algorithm takes the input from a camera. LiDAR, WIFI/Bluetooth models, and Ultrasonic sensors have been used. Inculcated OpenCV which has an advanced built-in function that has add-on provisions in extension to the blurred image, a threshold that can go low and high and determines whether there is an edge in the path or not. The input collected from the sensors was analyzed to give directions to the car on where to get a turn. In summary, the proposed autonomous solar car model is inexpensive to build and performs well. © 2025 the Author(s).

关键词： Ultrasonic sensors

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Development of A Cloud Storage Based Data Logging System

Development of A Cloud Storage Based Data Logging System

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Wasif, Shaheen Wasim, Saif Satvaya, Parthasarathi Jadavpur University School of Illumination ScienceEngineering and Design Kolkata India JLD Engineering and Management College Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798331516666

In the present world, as Internet of Things (IoT) based sensor monitoring and conditional controlling technology has been embedded in our day-to-day lives, there is a need for logging and storing sensor data for big data analysis. Due to free and easy to use features, Google spreadsheet has been chosen as the Cloud-based spreadsheet platform for sensor data logging and storing. This paper has provided a procedure to monitor sensor data, located at different geographical locations onto their corresponding specified worksheet in a well-defined structured manner in a single Google spreadsheet. The sensor readings uploaded in the Google spreadsheet can be accessed online by smartphone or a personal computer. For stable and accurate readings of sensors which work best on 5 V, the hardware communication protocol, Universal Asynchronous Receiver/Transmitter (UART) has been used during the development of the present work and this paper shows a simplistic way for uploading sensor values on the web, where memory constraint for storing sensor data is absent. In this developmental work, Arduino has been used for sensor data monitoring and ESP32 has been used for Internet connection © 2024 IEEE.

关键词： Cloud platforms

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Air Monitoring with Cloud and IoT

Air Monitoring with Cloud and IoT

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kalaivani, K. Subramanian, S. Swedha, G.K.S. Vinoth, N. Vishnu Priya, V. Sns College of Engineering Department of Computer Science and Design Coimbatore India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

The air is an essential aspect of humans' daily lives as it helps in breathing. With the rise of air pollution, it is necessary to have an efficient air quality monitoring system that can detect the concentration of pollutants in the air. In this study, the design and implementation of air quality monitoring system is discussed which uses an MQ2 sensor and ESP32 microcontroller. The system detects the ppm levels in the air to classify whether it is fresh or poor air. The challenges include establishing reliable connectivity, visualizing data effectively, integrating multiple sensors, ensuring accurate measurements, and enabling real-time analysis. The ESP32 microcontroller addresses connectivity issues by providing built-in Wi-Fi and Bluetooth capabilities. The LCD display offers real-time visual feedback, enhancing data visualization. Integration of the DHT11 sensor for temperature and humidity measurement and the MQ2 sensor for gas detection enables comprehensive air quality monitoring. The ThingSpeak application acts as a cloud-based IoT platform, allowing data logging, remote monitoring, and real-time alerts and notifications. If the ppm levels are less than 1000, it is considered poor air, and a buzzer is triggered, along with an LCD I2C display that shows the levels using the I2C protocol. The system also uses a DHT11 sensor to detect temperature and humidity and passes all data to the cloud channel by using IoT, triggering a mail alert using Google sheet with an on and off button for ppm levels using Javascript extension and GPS. The system is designed to provide a real-time solution for monitoring air quality, making it an essential tool for public health and environmental protection. © 2023 IEEE.

关键词： Microcontrollers

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Rural Retail Revolution AIML Driven Voice-Based Billing System in Kannada 8

Rural Retail Revolution AIML Driven Voice-Based Billing Syst...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sharvani, G.S. Patil, Channaveeragouda K. Chavan, Krishna S. Kalakatti, Nandeesh J. Kulkarni, Seema R. Patil, Soumya Visvesvaraya Technological University Tontadarya College of Engineering Department of Computer Science and Engineering Gadag India Visvesvaraya Technological University Tontadarya College of Engineering Department of Computer Science and Design Gadag India

ISBN: (纸本)9798331505462

The aim of the voice-based billing system in Kannada is traditional billing by leveraging speech recognition. This project focuses on creating an efficient system for generating bills and transactions using spoken Kannada. Gaps in current systems include language barriers, limited accessibility, and timeconsuming processes. The methodology involves data collection for training the voice recognition model, developing robust speech recognition using machine learning, implementing NLP for command understanding, designing a user-friendly interface, recommendations of items based on previous orders of the customer and integrating with the billing system. Expected outcomes include enhanced accessibility for Kannada speakers, improved efficiency, user satisfaction, cultural inclusivity, and streamlined transactions. By addressing these gaps and leveraging technology, this proposed system aims to transform the billing domain with inclusivity and innovation. © 2024 IEEE.

关键词： Natural language processing systems

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Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach

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SN computer science 2024年第1期5卷 45页

作者： Nalavade, Jagannath E. Patil, Akshta Buchade, Amar Jadhav, Nagesh Department of Computer Science and Engineering MIT Art Design and Technology University School of Computing Pune India Department of Computer Engineering Rajarambapu Institute of Technology Sakharale Sangli India

In light of recent incidents involving the leakage of private photographs of Hollywood celebrities from iCloud, the need for robust methods to safeguard image content has gained paramount importance. This paper addresses this concern by introducing a novel framework for reversible image editing (RIT) supported by reversible data hiding with encrypted images (RDH-EI) techniques. Unlike traditional approaches vulnerable to hacking, this framework ensures both efficient and secure data embedding while maintaining the original image’s privacy. The framework leverages two established methods: secret writing and knowledge activity. While secret writing is susceptible to hacking due to the complex nature of cipher languages, RDH-EI-supported RIT adopts a more secure approach. It replaces the linguistic content of the original image with the semantics of a different image, rendering the encrypted image visually indistinguishable from a plaintext image. This novel substitution prevents cloud servers from detecting encrypted data, enabling the adoption of reversible data hiding (RDH) methods designed for plaintext images. The proposed framework offers several distinct advantages. Firstly, it ensures the confidentiality of sensitive information by concealing the linguistic content of the original image. Secondly, it supports reversible image editing, enabling the restoration of the original image from the encrypted version without any loss of data. Lastly, the integration of RDH techniques designed for plaintext images empowers the cloud server to embed supplementary data while preserving image quality. Incorporating convolutional neural network (CNN) and generative adversarial network (GAN) models, the framework ensures accurate data extraction and high-quality image restoration. The applications of this concealed knowledge are vast, spanning law enforcement, medical data privacy, and military communication. By addressing limitations of previous methods, it opens new avenues

关键词： Deep hiding Deep neural network GAN model Reversible data hiding (RDH)

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