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Smart Security Management using IoT and HC-05 Bluetooth Module

Smart Security Management using IoT and HC-05 Bluetooth Modu...

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International Conference on Inventive Systems and Control (ICISC)

作者： S Sam Michael J Ancy Jenifer Ruth Moly Benjamin Cyril Jacob S Shanthosh Department of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

Safety and security are essential for every individual worldwide, particularly in the context of home security. Modern advancements in science and technology have introduced various innovative solutions, including smart security locking mechanisms. This research study presents a novel smart lock system that leverages mobile phone applications integrated with built-in fingerprint authentication. Unlike other smart locks that rely on external biometric devices, this system utilizes the smartphone’s internal fingerprint sensor, thereby reducing hardware costs and complexity. Additionally, the application supports multiple authentication methods, such as fingerprint and facial recognition, enhancing security and convenience. This solution eliminates the risk of key loss and ensures that only authenticated users can access the home. The lock mechanism operates automatically, allowing the door to open and close without human intervention, providing a seamless and secure user experience.

关键词： Bluetooth Costs Face recognition Authentication Fingerprint recognition User experience Software

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Bread Quality Assessment Using Deep learning with shape and Volume Metrics

Bread Quality Assessment Using Deep Learning with shape and ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Rohan Joel J Ruth Moly Benjamin Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector.

关键词： Deep learning Measurement YOLO Productivity Ethics Shape Quality control Real-time systems Quality assessment Standards

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Application of Blockchain technology in Ontology Management for Enhanced Data Security in Web Platforms 2

Application of Blockchain Technology in Ontology Management ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Aswini, Kilaru Rana, Ajay Reddy, Uma Nagpal, Amandeep Praveen Mohammed, Abdulkareem. M. Institute of Aeronautical Engineering Dundigal Hyderabad India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

Blockchain technology presents a transformative approach to ensuring data integrity, security, and trust in web platforms. This research explores the innovative application of blockchain as an underlying layer in ontology management systems to revolutionize data security and privacy. Ontologies, as structured frameworks for organizing information, are pivotal in enhancing understanding and interoperability among diverse web technologies. Integrating blockchain with ontology management involves creating immutable and verifiable records of ontological changes, access, and transactions. This ensures a transparent and secure mechanism for handling data, addressing the vulnerabilities inherent in centralized and less secure data management systems. The paper delves into architectural design, outlining how blockchain's decentralized nature and cryptographic algorithms contribute to robust ontology management. It presents a comparative analysis of existing data security mechanisms and illustrates the superior safeguards afforded by the blockchain-ontology model. Furthermore, the research highlights case studies from web platforms that have integrated this model, demonstrating notable improvements in data security, trust, and efficiency. The implications of this integration extend beyond enhanced security;they include improved data provenance, quality, and a reduction in fraudulent activities. This paper aims to catalyze further innovation and adoption of blockchain in web technologies, particularly in ontology management, to create a more secure and trustworthy digital ecosystem. © 2024 IEEE.

关键词： Data integrity

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： R Manoranjitham Somu Santhi Swaroop Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121’s dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2’s sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2’s superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity.

关键词： Measurement Deep learning Deepfakes Accuracy Computer architecture Feature extraction Data augmentation

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The Role of IoT and machine learning in Automating Space Docking: Challenges, Advancements, and Future Prospects

The Role of IoT and Machine Learning in Automating Space Doc...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and machine learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with SpaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made.

关键词： Space vehicles Mars Space missions Reinforcement learning Prediction algorithms Real-time systems Sensors Internet of Things Next generation networking Monitoring

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Brain Tumor Detection using YOLO v7 and YOLO v8

Brain Tumor Detection using YOLO v7 and YOLO v8

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Rohan Vaghela Jigar Sarda Amit Thakkar Darsh Vaishnani Avani Khokhariya Pooja Mehta Department of Computer Science & Engineering Charotar University of Science & Technology Anand India Department of Artificial Intelligence & Machine Learning Charotar University of Science & Technology Anand India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

The architecture of the brain is incredibly complex, with multiple areas managing various parts of the nervous system. An adult’s health is greatly at risk from brain tumors because of their ability to severely impair organ function or possibly cause death due to their rapid growth of abnormal brain cells. Brain tumors can develop in any area of the skull or brain, including the protective layer around the brain and many other areas. Since these tumors can have a wide range of shapes, sizes, and locations, magnetic resonance imaging (MRI) is an essential tool for locating malignant tumors. MRIs are examined manually in order to diagnose brain tumors. However, manually identifying brain tumors is a challenging and time-consuming task that may result in errors. As a result, computer-aided brain tumor detection has been considered a crucial area of study in biomedical engineering. However, because tumor sizes and shapes vary enormously throughout subjects, developing a reliable brain tumor detector remains difficult. Thus, for the purpose of accurately identifying brain tumors in MRI scans, two popular deep learning detection algorithms—YOLO v7 and YOLO v8—were tested and compared in this study. From the results, YOLO v8 model has achieved highest validation accuracy of 96.50% with average precision and recall value of 95.9% and 93.3% respectively, while YOLO v7 has validation accuracy of 87.6% with average precision and recall value of 81.9% and 85.4% respectively.

关键词： YOLO Training Accuracy Shape Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Tumors Testing

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction

ML for Sustainable Solutions: Applications in Renewable Ener...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Arya Awachat Ananya Dube Shivnath Chaudhri Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future.

关键词： Renewable energy sources Climate change Predictive models Wind energy Collaboration System integration Smart grids Sustainable development Optimization Energy storage machine learning

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Ensemble of Vision Transformers and CNNs for Accurate Diabetic Foot Ulcer Classification

Ensemble of Vision Transformers and CNNs for Accurate Diabet...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Arnold Anand Pagadala Salaja Silas Emmanuel Joy Division of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Diabetic Foot Ulcers (DFU) are a significant diabetes complication that can lead to lower limb amputation. Currently, 537 million suffer from diabetes worldwide and it is anticipated to rise to 783 million by 2045. Given the speed at which DFU is developing, prompt action is necessary to avoid the serious consequences of amputation and associated various medical conditions. With the evolution of image-based ML algorithms, automated methods for identifying and assessing DFUs are becoming more common. Existing research works on visual computing techniques concerns tissue classification and detects the DFU's visual appearance. This research study combines ResNet50 with Vision Transformers (ViT) and MobileNet with Vision Transformers (ViT) to develop an ensemble model to classify the existence or absence of a Diabetic Foot Ulcer (DFU). Experimental results of the proposed model when tested on challenging datasets achieved a validation accuracy of 98.6%.

关键词： Visualization Computational modeling Neural networks Transformers Diabetes Classification algorithms Cognitive robotics

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The Role of IoT and machine learning in Automating Space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

关键词： Reinforcement learning

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Role of machine learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 IEEE.

关键词： Global warming

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