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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Karthik S Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder.

关键词： Deep learning Image processing Teeth Cameras Real-time systems Mobile applications Monitoring Diseases

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Enhanced Security Through Distributed Fragmentation And Encryption For Cloud-Based Data Storage

Enhanced Security Through Distributed Fragmentation And Encr...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Kaushik J Jensen Jose C R. Vasanthi Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This paper proposes a novel cloud data security paradigm for sensitive applications, integrating Equipartition File Segmentation (EFS), a hybrid encryption scheme, and distributed storage. EFS partitions files into uniform fragments, subsequently encrypted using alternating AES and Blowfish algorithms in Cipher Block Chaining (CBC) mode with unique Initialization Vectors (IVs). To ensure secure key management, an RSA key pair is generated; the AES/Blowfish keys are encrypted with the RSA public key and persisted within Firebase Firestore. Notably, the private RSA key is encrypted using a user-supplied password and stored within the Firebase Database alongside the public key. Encrypted file fragments are distributed across Amazon S3 and Azure Blob Storage to enhance data availability and resilience. File reconstruction is performed by retrieving the RSA-encrypted symmetric keys and the password-encrypted private RSA key, decrypting the private key, and subsequently decrypting and reassembling the file fragments through Sequential Data Reassembly. This architecture achieves enhanced data security through robust cryptographic techniques, secure key management protocols, and distributed storage, while maintaining acceptable performance characteristics.

关键词： Cloud computing Accuracy Distributed databases Public key Vectors Encryption Cryptography Reliability Standards Resilience

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Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification

arXiv

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arXiv 2024年

作者： Patra, Aswini Kumar Sahoo, Lingaraj Dept. of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Itanagar India Dept. of Bio-Science and Bio-Engineering Indian Institute of Technology Guwahati India

Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time field applications requires algorithms specifically designed for the complexities of natural agricultural conditions. Our work proposes a novel deep learning framework for classifying drought stress in potato crops captured by UAVs in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages the pre-trained network’s feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work involves the integration of Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. Grad-CAM sheds light on the internal workings of the deep learning model, typically referred to as a"black box." By visualizing the model’s focus areas within the images, Grad-CAM fosters interpretability and builds trust in the model’s decision-making process. Our proposed framework achieves superior performance, particularly with the DenseNet121 pre-trained network, reaching a precision of 97% to identify the stressed class with an overall accuracy of 91%. Comparative analysis of existing state-of-the-art object detection algorithms reveals the superiority of our approach in significantly higher precision and accuracy. Thus, our explainable deep learning framework offers powerful approach for drought stress identification with high accuracy and actionable insights. Cop

关键词： Cams

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Duality between Lines and Points

arXiv

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arXiv 2025年

作者： Saxena, Sanjeev Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur208 016 India

There are several notions of duality between lines and points. In this note, it is shown that all these can be studied in a unified way. Most interesting properties are independent of specific choices. It is also shown that either dual mapping can be its own inverse or it can preserve relative order (but not both). Generalisation to higher dimensions is also discussed. An elementary and very intuitive treatment of relationship between arrangements in d + 1 dimensions and searching for k-nearest neighbour in d-dimensions is also given. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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A 10-Bit Successive Approximation Register Analog to Digital Converter Using VCO-Based Time-Domain Comparator

A 10-Bit Successive Approximation Register Analog to Digital...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ko-Chi Kuo Jia-Wei Chen Dept. Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The successive approximation register (SAR) analog-to-digital converter (ADC) architecture is employed to achieve low power consumption, allowing it to operate at low supply voltage. A time-domain quantization architecture based on a time-domain comparator is combined to overcome the design bottlenecks caused by the reduced intrinsic gain of transistors, the increased process variation, and the decreased supply voltage in advanced processes. A voltage-to-time converter based on a voltage-controlled oscillator (VCO) is used, providing the benefits of circuit stability and low noise impact. This design can be further improved by adding an SR time amplifier to the output of VCO, allowing the phase difference to grow exponentially. Hence, a faster escape from the dead zone of the phase detector can be realized to resolve the issue between high resolution and high speed in traditional VCO comparators. To accommodate the output generated by the improved voltage-to-time converter, the operation mode of the phase detector is modified, enabling it to be applied to the new voltage-to-time converter output. This paper presents a 10-bit SAR ADC in TSMC 40nm process, with a conversion rate of 10MS/s, a supply voltage of 0.9V, power consumption of 57.9µW, an ENOB of 9.88 bits, an SFDR of 74.07dB, and a FoM of 6.14 fJ/conv.-step.

关键词： Quantization (signal) Power demand Voltage-controlled oscillators Noise Detectors Voltage Registers Circuit stability Time-domain analysis Analog-digital conversion

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Signals of Opportunity for Car Density Estimation with Limited Training Data

Signals of Opportunity for Car Density Estimation with Limit...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Al Amiri, Wesam Abdelsalam, Omar Jones, James T. Guo, Terry N. Mackenzie, Allen B. Tennessee Technological University Dept. of Electrical and Computer Engineering CookevilleTN United States Tennessee Technological University Dept. of Computer Science CookevilleTN United States Center for Manufacturing Research Tennessee Technological University CookevilleTN United States

ISBN: (纸本)9798350335590

Passive sensing leverages existing signals from illuminators of opportunity to perform target localization, detection, and tracking without the need for additional infrastructure. In this paper, we explore the use of WiFi signals of opportunity for estimating car density in parking lots. The aim is to develop an efficient and cost-effective parking occupancy estimation system to alleviate traffic congestion caused by drivers searching for parking spaces. To achieve this, experimental measurements were conducted in a real outdoor environment to collect reflected WiFi signals from targets (cars). These collected signals were then used for car density estimation using a combination of semi-supervised learning convolutional neural network (CNN) and weighted-centroid interpolation techniques, only requiring small size datasets and limited measurements. The proposed method overcomes the limitations of existing data-driven estimators by reducing the reliance on large labeled datasets and computational complexity associated with traditional supervised learning methods. In addition, it provides a cost-effective alternative to the traditional systems that rely on a large number of sensors. Simulations are performed to evaluate the performance of the estimation, and the results demonstrate that our scheme can effectively estimate car densities in the parking lot with reasonable estimation errors. © 2023 IEEE.

关键词： Convolutional neural networks

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering Systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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Child and Women Safety Measures using iBeacon Technology

Child and Women Safety Measures using iBeacon Technology

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Shanker Shalini Keerthana E Nishanthi P Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

A smart real-time tracking system featuring a compact wearable device is designed to enhance the safety of children and women by utilizing IoT-driven technology and a secure database for efficient movement monitoring. This system operates on four key pillars: outdoor localization, cross-platform communication, server integration, and user engagement. The tracking mechanism comprises a wearable device embedded with an iBeacon and a built-in MEMS accelerometer, seamlessly linked to a smartphone application. The system begins with sensor calibration to establish reference values, followed by continuous tracking of sensor variations, particularly accelerometer data, to detect motion patterns with precision. Once paired with the designated Android application, the wearable device analyze movement data and transmits GPS coordinates to a centralized server, ensuring real-time location tracking of both the device and the associated iBeacon. Planned future improvements include the integration of Google Maps for enhanced real-time visualization, interactive monitoring, geofencing alerts, and emergency response notifications, making the system more robust and responsive to safety concerns.

关键词： Accelerometers Tracking Web services Real-time systems Threat assessment Software Safety Servers Wearable devices Monitoring

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Machine Learning Based Potential Customer Recommendation System for E-Commerce Using Feature Selection and XAI

Machine Learning Based Potential Customer Recommendation Sys...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Zahrul Jannat Peya Md. Shymon Islam Nusrat Jahan Urme Saiful Islam Auny Dept. of Computer Science and Engineering North Western University Khulna Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In this era of digital commerce, systems that provide personalized recommendations are paramount in enhancing user experience and facilitating sales on online commerce platforms. For this reason, merchants need to know their potential customers. This work is devoted to developing a potential customer recommendation system using the well-known customer behavior and shopping habits Kaggle dataset. A customer is considered as potential customer having purchase amount greater than 70 USD and a review rating of at least 4. One of the contributions of this work is the utilization of the Select K-best with Mutual Information (MI) feature selection method in the e-commerce recommendation system. This study proposes a smart customer recommendation system using Machine Learning (ML) and Explainable AI to help merchants make data-driven decisions on which customers are the most potential ones. The proposed work makes the best use of several machine learning algorithms including Random Forest, Logistic Regression, and K-Nearest Neighbor. Among these models, Random Forest reveals the best performance of 100% accuracy. In addition, the Local Interpretable Model-agnostic Explanations (LIME) from Explainable AI are used to detect the responsible transaction records, product features and purchasing behavior of the consumers.

关键词： Visualization Accuracy Explainable AI Computational modeling Feature extraction User experience Electronic commerce Recommender systems Random forests Mutual information

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Facial Forgery-Based Deepfake Detection Using Fine-Grained Features 22

Facial Forgery-Based Deepfake Detection Using Fine-Grained F...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Nadimpalli, Aakash Varma Rattani, Ajita School of Computing Wichita State University Kansas United States Uni. of North Texas at Denton Dept. of Computer Science and Engineering TX United States

ISBN: (纸本)9798350345346

Facial forgery by deepfakes has caused major se-curity risks and raised severe societal concerns. As a counter-measure, a number of deepfake detection methods have been proposed. Most of them model deepfake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based methods have demonstrated very high efficacy in deepfake detection with the Area under the Curve (AUC) as high as 0.99. However, the performance of these methods degrades signifi-cantly when evaluated across datasets and deepfake manipulation techniques. This draws our attention towards learning more subtle, local, and discriminative features for deepfake detection. In this paper, we formulate deepfake detection as a fine-grained classification problem and propose a new fine-grained solution to it. Specifically, our method is based on learning subtle and generalizable features by effectively suppressing background noise and learning discriminative features at various scales for deepfake detection. Through extensive experimental validation, we demonstrate the superiority of our method over the published research in cross-dataset and cross-manipulation generalization of deepfake detectors for the majority of the experimental scenarios. © 2023 IEEE.

关键词： Cross-Manipulation Generalization Deepfakes Facial Manipulations Fine-Grained Classification

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