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Global Conference for Advancement in technology (GCAT)

作者： Gangadhar Tejesh Rashmi Karchi Sanjeevakumar Hatture Asharani Sankanagoudra Rashmi Saini Department of Artificial Intelligence and Machine Learning Nagarjuna College of Engineering and Technology Bengaluru Karnataka India Department of Information Science and Engineering Nagarjuna College of Engineering and Technology Bengaluru Karnataka India Department of Computer Science G. B. Pant Institute of Engineering and Technology Uttarakhand India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Facial recognition is a challenging task, especially in the wild where there exist a substantial amount of variation in lighting, pose, and expression. One-shot knowledge acquisition is a form of machine learning that allows a model to learn to recognize a new class Upon observing just a single instance of that class. the proposed work of a one-shot learning strategy for facial recognition system that is specifically designed to detect persons with or without spectacles. The proposed work approach has the capability to achieve high accuracy on a limited dataset of labeled images, and possesses the capacity to adjust to unfamiliar images that were not encountered in prior training stages. In this work, One-shot learning aims to recognize individuals based on a limited number of examples with and without specs, making it suitable for scenarios where acquiring a large dataset is challenging.

关键词： Training Accuracy Face recognition Surveillance Knowledge acquisition Neural networks Lighting One shot learning Medical services Reliability

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Vortex Search Optimizer based Hybrid Watermarking Scheme for Securing the Medical Images

Vortex Search Optimizer based Hybrid Watermarking Scheme for...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： G N Keshava Murthy Nalini B M Madhura G K Nayana S Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka Department of Computer Science and Engineering East West Institute of Technology Bengaluru Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

Preventing the duplication, modification, and claim of unauthorised ownership of digital content is one of the major issues caused by the growing use of computer capabilities. Watermarking is one way to put a visible or invisible watermark on a digital document in order to later show that the document is legitimate or belongs to its owner. concentrating on security issues in the storage and broadcast of images in the medical info scheme, as well as the special requirements for medical images for the protection of lesion regions. In this study, a hybrid watermarking strategy was presented utilising the Slantlet transform, randomised singular value decomposition, and optimisation methods (Vortex Search algorithm (VSA)) that are modelled after natural processes. The XOR encryption method is used to encrypt the watermark image. Extensive testing demonstrates that our novel approach works better than the current approaches based on the NC, SSIM, and PSNR. The PSNR of the suggested technique is between 58 and 59 dB, suggesting increased performance, and at a scale factor of 0.06, the SSIM and NC standards of the watermarked picture and the extracted watermark are nearly equal to. The recommended method outperforms past studies in the field in terms of usability, robustness, security, and invisibility, as well as its ability to repel local nonlinear geometric attacks.

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IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through machine learning 3

IoT-based Efficient Waste Management on University Campus: E...

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3rd International Conference on machine learning and Data engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector machine (SVM)'s flawless

关键词： Electronic Waste

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Zayd Balassem Ramya Srikanteswara Esakki Madura E Madugula Anjaneyulu M Devi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed_established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM.

关键词： Support vector machines Accuracy Neural networks Noise Power system stability Logic gates Feature extraction Smart grids Data mining Convolutional neural networks

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Support Vector machines for Soil Quality Assessment in Sustainable Farming 7

Support Vector Machines for Soil Quality Assessment in Susta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Nainwal, Ankita Alabdeli, Haider Varshney, Neeraj Deepak, A. Reddy, N. V. Uma Reddy, K. Jyothsna Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to understand the soil's health, fertility, and crop appropriateness, soil quality assessments are an important part of sustainable agricultural techniques. Using their accuracy and resilience in dealing with complicated, nonlinear datasets, Support Vector machines (SVMs) are investigated in this research as a potential tool for soil quality evaluation. Our study presents a comprehensive methodology for using SVM to analyze soil properties, including pH, nutrient content, moisture levels, and organic matter. We employ a dataset encompassing diverse soil samples from different geographical regions and farming conditions. The proposed SVM-based model is trained and validated using this dataset, demonstrating high predictive accuracy and reliability in classifying soil quality. Additionally, we compare the performance of SVM with other machine learning techniques such as Random Forest and k-Nearest Neighbors, highlighting the advantages of SVM in terms of precision and computational efficiency. The results indicate that SVM can serve as a powerful tool for farmers and agronomists, facilitating informed decision-making and promoting sustainable agricultural practices. In conclusion, this research underscores the potential of SVM in soil quality assessment, contributing to the advancement of precision agriculture. Future work will focus on integrating SVM with remote sensing technologies and expanding the dataset to include more diverse soil types and environmental conditions. © 2024 IEEE.

关键词： Support vector machines

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A Review of machine learning and Deep learning algorithms for Chronic Kidney Disease Prediction

A Review of Machine Learning and Deep Learning algorithms fo...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Pratik Nagre Prateek Verma Praveen Kumar Nitin L. Gavankar 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 Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Walchand College of Engineering Sangli Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of machine learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes.

关键词： Deep learning machine learning algorithms Accuracy Computational modeling Medical treatment learning (artificial intelligence) Prediction algorithms Chronic kidney disease Data models Diseases

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Empirical Evaluation of a Cybersecurity Powered Human Body Data Communication over Wireless Body Area Network 1

Empirical Evaluation of a Cybersecurity Powered Human Body D...

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1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Mabel Rani, A. Jaya Ramachandran, Lalitha Sangeethapriya, S. Samuthira Pandi, V. Maniraj, K. Anni Princy, B. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Mathematics Tamil Nadu Chennai India S.A. Engineering College Department of English H&S Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (纸本)9798350365016

All contemporary, user-friendly electronics necessitate technologies that allow for close-range communication between humans and other devices. In this paper, we show how to build and deploy a Human Area Network, which allows us to communicate with one another face-to-face. It is now crucial for mobile terminals to communicate with terminals embedded in the environment. The usage of wires is cumbersome since they often become tangled and are a pain to maintain when communicating between nearby devices. Data rates are lowered in congested settings, such exposition grounds, when extremely weak radio signals are utilized for communication due to packet collision. Additionally, unauthorized signal interception poses a security concern. In order to solve these kinds of challenges, we employ the human body as a means of communication. Cybersecurity Powered Human Data Communication (CPHDC) is a new communication logic introduced in this paper that uses the Red Tacton device to achieve threat- and error-free communication. To test how well it works, it is cross-validated with Frequential Band based Data Communication (FBDC), a conventional communication scheme. Using wearable devices for identification and data transmission to the master device in real time, the suggested model offers a safe and efficient communication system. With its integrated CPU and insulating material (copper) that enable intra-body communication through galvanic coupling, the suggested model is easily worn on the wrist or any other area of the body. Therefore, in comparison to existing wireless technologies, the suggested human body approach improves the security of sent signals. © 2024 IEEE.

关键词： Cybersecurity

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Reduction of PAPR in OTFS signal using airy special function

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Journal of Optical Communications 2024年第s1期45卷 s2125-s2131页

作者： Bitra, Hanumantharao Anusha, Chillapalli Pradhan, Nrusingha Charan VIT-AP University Amaravati Campus Andhra Pradesh Amaravati India Department of Computer Science and Engineering Artificial Intelligence and Machine Learning Vasireddy Venkatadri Institute of Technology Andhra Pradesh Nambur India Engineering Optimization and Modeling Center Reykjavik University Reykjavik102 Iceland

Orthogonal time frequency space (OTFS) is a new two dimensional modulation technique that use the delay-Doppler domain. Despite the fact that OTFS can handle time varying channels and high Doppler scenarios, it has a high Peak-to-Average Power Ratio (PAPR). In this study, we developed an airy-based companding technique for the OTFS signal to address this issue. The airy function is a particular function that is commonly used in optics. We evaluate the proposed airy based companded OTFS with various companding techniques in order to assess its performance. Simulations signify that the proposed airy based companding outperforms the rooting companding, and μ - law companding in terms of both PAPR and instantaneous to average power ratio (IAPR). © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Peak to average power ratio

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Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

arXiv

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arXiv 2025年

作者： Wang, Dong Haag, Hannes Herraez, Daniel Casado May, Stefan Stachniss, Cyrill Nüchter, Andreas Informatics XVII – Robotics Julius-Maximilians-Universität Würzburg Germany Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Germany CARIAD SE University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://***/Wayne-DWA/Doppler-SLAM. © 2025, CC BY-NC-ND.

关键词： Mapping

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Advancements in Immunization Management for Personalized Vaccine Scheduling with IoT and machine learning 10

Advancements in Immunization Management for Personalized Vac...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Madheswaran, M. Dhanalakshmi, R. Ramasubramanian, G. Aghalya, S. Raju, S. Thirumaraiselvan, P. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Immunization is essential to public health;currently, comprehensive vaccine schedules may not sufficiently address individual health profiles. The Internet of Things (IoT) collects real-time health data, and decision trees (DTs) develop personalized vaccination schedules. Wearable health monitors and electronic health records provide continuous health data and information. DTs are customizable machine learning (ML) algorithms that assess this data and create immunization regimens. Based on age, vaccination history, underlying health issues, and real-time health measurements, DTs provides individualized vaccine recommendations. More vaccination compliance and greater health needs alignment are the system's main benefits. Based on real-time data, this technique recommends vaccination administration timings. Optimization of vaccination systems reduces vaccine-preventable illness transmission. Integrating IoT data, preprocessing, and vaccination scheduling using DTs are covered in this work. It emphasizes data security and ethics while managing sensitive health information during privacy and security issues. Personal vaccination scheduling using IoT and DTs advances public health. Regulations that protect privacy and provide equal access to personalized vaccination schedules are essential as the system matures. This novel method might save lives, save healthcare costs, and improve vaccination systems. © 2024 IEEE.

关键词： Vaccines

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