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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

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Visionary Insights: CNN-Driven Retinal Vessel Segmentation in Ophthalmic Imaging

Visionary Insights: CNN-Driven Retinal Vessel Segmentation i...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sivagami, S. Gopinath, M. Yuktha Varshika, J. Srivarshinie, V. Monikaa, R. Senthil, K. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Information Technology Tamil Nadu Chennai602105 India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Artificial Intelligence And Machine Learning Tamil Nadu Chennai602105 India Easwari Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai600089 India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Retinal vessel segmentation plays a vital role in the analysis of ophthalmic imaging. Binary Segmentation is one type of segmentation that segments the retinal vessel and blood vessel in retinal images. Retinal vascular geometry reflects a patient's state of health and aids in the diagnosis of certain conditions, including hypertension and diabetes. Early detection of these diseases helps to prevent blindness of patients. machine learning and Deep learning are two important sectors in artificial intelligence. Nowadays, Deep learning algorithms are very much used in Medical Image processing. These Deep learning Algorithms are more accurate and efficient than manual segmentation and other computer-aided diagnosis methods. Especially Convolutional Neural Networks (CNNs) in Deep learning Architecture are used for the segmentation process. CNN architecture can handle the complex characteristics of retinal images. Traditional Data augmentation techniques are used to enlarge the training and testing samples. Matched filter methods are used to remove the noise and Morphological operations are implemented to detect the abnormal regions in the retinal image. Our proposed model achieves an accuracy of 97.87%, precision of 96.8%, Intersection over Union of 97.1%, and specificity of 0.5% in the segmentation process. The proposed CNN architecture not only expedites the segmentation process but it makes advancements in clinical decisions in the field of ophthalmology. © 2024 IEEE.

关键词： Matched filters

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A Review on the Use of AI and Robotics in Agricultural Practices

A Review on the Use of AI and Robotics in Agricultural Pract...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Mohan K. Warbhe Prateek Verma Rajendra Rewatkar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Robotics and artificial intelligence are becoming a part of the agricultural sector in ways bringing a change in it. It is evident that different technologies can help improve the yields, increase sustainability and efficiency in farming. This paper aims at offering a brief on the use of AI and robotics in agriculture; this will include using technologies such as Exact Hybrid Precision Farming, AI in livestock farming, AI-based resource supply chain applications, and others. In this literature review, the ideas of other authors are compiled and compare the concepts of culture as a factor in tackling the issues affecting agriculture and the possibility of utilizing artificial intelligence in the process. The theoretical literature review of the application of AI and robotics in precision farming aims at presenting literature evidence showcasing the use of activities such as predictive analytics, decision support systems, automated crop monitoring, and robotic harvesting. Other discussed issues are threats including high capital investment during the setting up and implementation of the system, replacement of labor by machines, as well as data insecurities.

关键词： Training Costs Bibliographies Government Supply chains Agriculture artificial intelligence Sustainable development Robots Farming

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Digital Evidence Management: Modern Techniques in Digital Forensics 3

Digital Evidence Management: Modern Techniques in Digital Fo...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Venkataramanan, N. Jayalakshmi, V. Anitha, S. Ganesan, R. Jenifer, V. Drbccc Hindu College Department of Computer Applications Pattabiram Chennai600072 India Department of Computer Science and Applications Ramapuram600089 India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai600119 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Department of Computer Science and Applications Ramapuram Campus 600089 India

ISBN: (纸本)9798331509675

This research study reviews the significance of digital forensic analysis in modern criminal investigations, highlighting the way it complements conventional methods of inquiry and the difficulties it encounters in the information era. To solve crimes effectively, investigators must combine digital knowledge with traditional forensic techniques. This will enable them to use evidence from many different types of digital and physical sources. The specific intricacies of digital crime scenes need specialized methods for the proper storage and analysis of digital evidence to identify criminal intent and patterns of conduct. To propose Modern Techniques for Digital Evidence Management [MT-DEM] based Digital Forensics Service. Multidisciplinary teams and cutting-edge technology are becoming necessities in digital forensic investigation as the field adjusts to new threats like data encryption and massive amounts of data. The admissibility of evidence in the legal world depends on digital forensic procedures being transparent and adhering to set criteria. Digital forensic analysis has become an indispensable tool for contemporary investigators with the rise of digital evidence. © 2025 IEEE.

关键词： Forensic science

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African Vultures Based Feature Selection with Multi-modal Deep learning for Automatic Seizure Prediction

African Vultures Based Feature Selection with Multi-modal De...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur Sandhya M Zabiha Khan Mohan B R C P Nayana S A Rajashekhar Software Engineer Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures.

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation 6

Semantic Segmentation of Areal Images using Pixel Wise Segme...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Gowroju, Swathi Swathi, V. Narsimhulu, K. Choudhary, Shilpa Department of Artificial Intelligence and Machine Learning Sreyas Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science Sreyas Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science Matrusri Engineering College Telangana Hyderabad India Department of Computer Science NGIT Telangana Hyderabad India

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Semantic Segmentation

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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Computational intelligence and Computing Applications (ICCICA), International Conference on

作者： Shweta Jain Anju Bhandari Gandhi Stuti Mehla Rishit Aggarwal Tanu Kwatra Department of Artificial Intelligence and Machine Learning Panipat Institute of Engineering and Technology Panipat India Department of Computer Science and Engineering Panipat Institute of Engineering and Technology Panipat India Infosys Limited Pune India Department of Computer Science and Applications D.A.V Centenary College Faridabad India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future.

关键词： Headphones Virtual environments Streaming media Cameras Real-time systems Hardware Haptic interfaces

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A Review on the Applications of Pesticide Spraying Robots in Agricultural Practices

A Review on the Applications of Pesticide Spraying Robots in...

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engineering and technology (PICET), Parul International Conference on

作者： Mohan K. Warbhe Rajendra M. Rewatkar Prateek Verma Yash S. Pachghare Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

India is an agriculture based economy and it is one of the most significant sector in India. In traditional agriculture methods, there are various challenges such as reliance on manual labor and limited access to modern farming techniquies due to lack of advanced infrastructure. The spraying of pesticides pose a serious risk to human health as spraying contains potentially hazardous chemicals. This study provides a review on the various techniques being used for spraying pesticides in the agricultural fields. Robots can automate task like planting, weeding and harvesting with better performance in less time. Equipped with sensors and artificial intelligence (AI), they can collect the information to make the accurate decisions about soil, crop, health and weather in real time, thereby reducing costs and environmental impact while enhancing productivity. The robotic spraying provides the advantage over traditional methods as it has the ability to maintain its maximum capacity with best efficiency while the pesticide spraying system evenly cover the plants with spray in the appropriate quantities. This connectivity to smart agriculture systems emphasizes the technology's role as a pillar in the shift to efficient and sustainable farming methods. The deployment of these agricultural robots leads to reduction in risk of the potential hazards linked to human contact with dangerous substances and also supports sustainable agriculture.

关键词： Accuracy Reviews Spraying Crops Pesticides Robot sensing systems Agriculture Robots Water resources Farming

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Secured Heterogeneous Data Fusion Using Adversarial Autoencoders in Internet of Military Things

Secured Heterogeneous Data Fusion Using Adversarial Autoenco...

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Data Science and Business Systems (ICDSBS), International Conference on

作者： A Prabagaran S Kalavathi N M Balamurugan Angelin Blessy J Krishan Berwal John Ayeelyan Gupshup Technology India Pvt Ltd Bengaluru India Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai Department of Artificial Intelligence & Machine Learning Rajalakshmi Engineering College Chennai Dept of Computer Science and Engineering Medicaps Univeristy Indore MP India Dept of Informatics Faculty of Communication Engineering Military College of telecommunicationn and Engineering Mhow Indore MP

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Internet of Military Things (IoMT) is the intervention of IoT technology in the Military applications to gain more knowledge on the unmanned areas, the battlefield, and for warfare purposes. As such, IoT, IoMT also uses sensors, communication networks and protocols, and userspecific application processes. However, the IoMT requires more security and integrity in the data collected from the sensor network, as a huge amount of data is required to predict the scenario on which a decision has to be made. On the other hand, as the volume of data increases, it reduces the QoS of the whole system. Heterogeneous data fusion plays a vital role in representing multi-modal data into a singular format that has to be replicable. Data from various sources need to be securely fused so that the used information is highly reliable. Therefore, Adversarial Autoencoder is proposed for heterogeneous data fusion in the IoMT application to reduce the data dimensionality with good integrity and security. The proposed work outperforms the conventional method in terms of evaluation parameters and efficiently detects the threats using the anomaly detection using the IR Small dataset and EMI dataset from Kaggle.

关键词： Protocols Military computing Autoencoders Data integration Quality of service Sensor fusion Sensors Security Reliability Anomaly detection

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Passive RFID Encryption and Decryption using CLI

Passive RFID Encryption and Decryption using CLI

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： K L Sailesh Kumar M Srivatsan S.J. Suhas Kumar Sudarshan Satyaa J S Vishwamalyan Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

The implementation of the concept of RFID encryption to make a system that prevents the attack on the data of any user or organization on the cloud. The built application has a command line interface. The command line interface is used here instead of a web server to prevent data leakage during data transmission and reduce the risk of a rogue web server. The command line interface takes in an input image and encrypts it, producing an encrypted image and a key file. The CLI also decrypts the previously encrypted image using the key file. This app as a whole solves the problem of users and organizations that store images on the cloud, using this app the user can store encrypted images on the web while the low storage key file is on the user/organization’s on-site server. This provides a 2-tier security mechanism via which an adversary will need to hack both the organization server and the cloud server to compromise the data, this way the image data is secure without any additional cost or overhead to the organization. an RFID encryption algorithm is developed along with a non-network-based CLI that secures the organization's RFID data and helps implement a secure cloud-bursting architecture. In this project, an algorithm is developed by combining both the XOR-based encryption algorithm and non-network-based CLI to secure the RFID of the user/organization. Most encryption algorithms nowadays use web-based servers to encrypt the RFID and produce the key file. This makes web-based encryption insecure and vulnerable to a man-in-the-middle attack. Therefore, this app comes to the rescue as it provides a non-network-based CLI by which there is no need for the actual RFID to go outside of the local machine, thus making this app and its architecture secure and robust.

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Enhancing the Security by Analyzing the Behavior of Multiple Classification Algorithms with Dimensionality Reduction to Obtain Better Accuracy

Enhancing the Security by Analyzing the Behavior of Multiple...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Sai Nandini Sreenivasa Murthy Praksha P K Prakash U Suriya Prakash J Department of CSE JAIN(Deemed-to-be-University) Bengaluru India Department of ISE Rajarajeswari College of Engineering Bengaluru India Dept of Artificial Intelligence Machine Learning Rajarajeswari College of Engineering Bengaluru India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Intrusion detection in network traffic is a crucial component of modern cyber security strategies, aimed at identifying and mitigating malicious activities that threaten the integrity and availability of computer networks. With the proliferation of internet-enabled devices and the increasing sophistication of cyber threats, the importance of effective intrusion detection systems (IDS) cannot be overstated. This abstract provides an overview of the literature survey on intrusion detection in network traffic, which explores various techniques, algorithms, and methodologies employed in detecting and responding to cyber threats. Overall, this literature survey provides valuable insights into the complexities of intrusion detection in network traffic, offering readers a comprehensive overview of this critical aspect of cybersecurity. By understanding the latest trends, techniques, and challenges in intrusion detection, organisations can better protect their networks and mitigate the risks posed by cyber threats. Github link: https://***/pandhini13/intrusion-detection-and-network-traffic-

关键词： Surveys Support vector machines Accuracy Refining Intrusion detection Telecommunication traffic Predictive models Boosting Probabilistic logic Classification algorithms

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