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LIME Explainability on Flower Classification 6

LIME Explainability on Flower Classification

6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Nikith, Boda Venkata Nikhil, Masabattula Teja Sri Siddhartha, Mutyala Sai Murali, K. Amrita Vishwa Vidyapeetham Dept. Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Dept. Mathematics Bengaluru India

ISBN: (纸本)9781665456982

Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI. © 2022 IEEE.

关键词： Lime

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Enhanced Parkinson's Disease Prediction Using Feature Selection, SMOTE, and Machine Learning with Deep Learning Models

Enhanced Parkinson's Disease Prediction Using Feature Select...

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International Conference on computer and Information Technology (ICCIT)

作者： Al-Amain Mirza Nadim Saad Esrat Jahan Rajon Bardhan Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer & Cyber Science Augusta University Georgia United States

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research leverages the UCL Parkinson's Disease clinical dataset to predict Parkinson's illness with the application of both deep learning and machine learning techniques. To address class imbalance, SMOTE was applied, and feature selection was conducted using the sci2 tool. The study assessed six deep learning algorithms, such as LSTM, PLSTM, and MLP, as well as six machine learning classifiers, including SVM, KNN, Logistic Regression, Naive Bayes, Decision Tree, and AdaBoost. MLP outperformed the others in terms of accuracy for deep learning models. With 97.20% accuracy, 97.44% precision, 97.20% recall, and 97.32% F1-score, KNN was the most successful overall, nonetheless. These findings show how well KNN detects Parkinson's disease and emphasize the technology's potential for early diagnosis and monitoring.

关键词： Deep learning Support vector machines Accuracy Machine learning algorithms Parkinson's disease Nearest neighbor methods Predictive models Feature extraction Prediction algorithms Monitoring

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Modular Soft-switched PV Converter with an Efficiency Optimization Scheme for High Frequency Linking Power Balancers

Modular Soft-switched PV Converter with an Efficiency Optimi...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Kajanan Kanathipan John Lam Dept. of Electrical Engineering and Computer Science York University Toronto Canada

A modular photovoltaic (PV) step-up converter with embedded high frequency power balancers that utilize an interlinking controller capable of power efficiency optimization over a wide operating range for Medium Voltage (MV) DC distribution is proposed in this paper. The converter utilizes an integrated boost-CLL resonant circuit with power balancing active voltage quadruplers (AVQ) to achieve step-up voltage conversion, individual maximum power point tracking (MPPT) and modular power balancing. The proposed interlinking power balancer controller optimizes the transmission of mismatched power while simultaneously achieving soft-switching operation on all AVQ circuits. The modular nature of the system allows for additional modules to be added without requiring parameter changes to the existing system. Analysis of the proposed converter and power balancing technique along with 10kW, 10kV-output system simulation and preliminary results on a scaled down 500W, 850V-output system are provided to highlight the features of the proposed system.

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Automating Requirements Review in the Automotive Sector: A Tailored AI Approach

Automating Requirements Review in the Automotive Sector: A T...

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IEEE International Conference on Requirements engineering

作者： Sivajeet Chand Chang Li Cristina Martinez Montes Beatriz Cabrero-Daniel Jennifer Horkoff Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in de... 详细信息

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

关键词： Reviews Software Requirements engineering Artificial intelligence Automotive engineering

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Handwritten Character Recognition of Telugu Characters 8

Handwritten Character Recognition of Telugu Characters

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Wasalwar, Yash Prashant Singh Bagga, Kishan Bhogendra Rao, P.V.R.R. Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineerng Nagpur India DRDO Kanchanbagh Defence Research and Development Laboratory Hyderabad India

ISBN: (纸本)9798350334012

Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. The lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. This research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. The dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. The accuracies had followed a certain trend. The model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%. © 2023 IEEE.

关键词： Character recognition

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Automatic Fusion Network for Cold-start CVR Prediction with Explicit Multi-Level Representation 39

Automatic Fusion Network for Cold-start CVR Prediction with ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Jin, Jipeng Lu, Guangben Li, Sijia Gao, Xiaofeng Tan, Ao Wang, Lifeng Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Dept. of Computer Science and Engineering China Corporate Development Group Tencent China

ISBN: (纸本)9798350322279

Estimating conversion rate (CVR) accurately has been one of the most central problems in online advertising. Existing methods in production focus on learning effective interactions among features to boost the model performance. Despite great success, these methods treat all the features equally without distinction. However, different features suffer differently from cold-start issues. Tail elements in those high-cardinality features, which we denote as fine-grained features, tend to have inadequate samples and thus fail to obtain semantically meaningful embeddings. Interacting with those features leads astray and impairs the accuracy of new ads in a cold-start scenario. In this paper, we propose Automatic Fusion Network (AutoFuse) to better tackle the challenge. AutoFuse explicitly separates features into groups based on their granularity and learns multiple levels of representation conditioned on different combinations of feature groups. Concretely, AutoFuse learns an ad-level representation to depict the unique individual character and a group-level representation to portray the collective information by discarding the fine-grained features. The final robust and general ad representation is obtained by integrating these two level representations adaptively. Such a combination encompasses a wider amount of information, and thereby mitigates the cold-start issue. Extensive experiments on two industrial-scale datasets and three public datasets show that AutoFuse significantly and consistently outperforms a spectrum of competitive methods including our currently deployed model. Meanwhile, the remarkable improvement on new ads validates the effectiveness of our method in cold-start scenarios. We design AutoFuse as a generic approach and thus it can be seamlessly transferred into other domains. Our method has been deployed online to serve billions of users and ads and has achieved significant GMV gain of 2.84%. © 2023 IEEE.

关键词： Marketing

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Cost-efficient Blockchain-based e-KYC Platform using Biometric verification

Cost-efficient Blockchain-based e-KYC Platform using Biometr...

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International Conference on Information Networking

作者： Sahil Bhatia Lokendra Vishwakarma Debasis Das Computer Science & Engineering Dept. Indian Institute of Technology Jodhpur Rajasthan

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Know Your Customer (KYC) is the authentication and verification process of the user carried out in the financial sector by institutions such as banks, insurance companies, fintech companies, etc., and other sectors such as smart healthcare, real estate, telecommunication, online gaming and gambling companies before engaging in any financial activities. The KYC process is made mandatory for Financial Institutions (FIs) by governments worldwide to keep a check on terror financing and money laundering activities. However, the traditional physical KYC process has limitations such as high operational costs, large time consumption, privacy and security issues, repetitive processes across multiple institutions and user inconvenience. Hence, the FIs are looking for alternatives to the physical KYC. The digitization of the KYC process, known as e-KYC, was explored but had problems such as a single point of failure, repetition of KYC process across multiple FIs and lack of security. Blockchain-based e-KYC has become popular these days and caught the eye of FIs due to inherent properties of the blockchain such as decentralization, transparency, and immutable ledger. Moreover, blockchain-based solution is highly secure and cost-efficient. This paper proposes a blockchain-based e-KYC platform where users can register themselves and get their video KYC done after ether payment. Once successfully verified, the user will receive a KYC key. The user will submit this key to the banks or other FIs in which he wants to register. The FIs can verify whether the user has undergone the KYC process by looking in the blockchain using the KYC key. Our solution requires the user to submit his documents only at the e-KYC platform and not at different FIs for registration, thus preserving his privacy and, at the same time, ensuring the user’s authenticity.

关键词： Privacy Costs Government Insurance Companies Medical services Registers

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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Balancing Real-world Interaction and VR Immersion with AI Vision Robotic Arm 20

Balancing Real-world Interaction and VR Immersion with AI Vi...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yang, Xiaoran Zhan, Yang Iwasaki, Yukiko Shi, Miaohui Tang, Shijie Iwata, Hiroyasu Waseda University Dept. Modern Mechanical Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan Waseda University Dept. Computer Science and Engineering 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan

ISBN: (纸本)9798350320831

With the widespread adoption of Virtual Reality (VR) technology, VR devices can be more realistic and make the tasks they perform feel real to the user. It is this sense of realism that makes more and more people are using VR headsets. While using VR devices, the need to interact with the real world will also exist. For example, when people are using a VR headset to study and work, they have a physiological need (thirst, hunger, etc.), and need to interact with reality. But the closed nature of the VR headset causes this interaction to be very disruptive to the immersion of the user's current task. To address this issue, we propose an interaction method that combines an AI vision algorithm and a robotic arm to preserve immersion during task performance in VR. This method addresses the challenge of interacting with the real world while using closed VR devices. Our experiments show that our method outperforms see-through and mapping methods, allowing users to quickly re-immerse themselves after interruptions and reducing interruptions during real-world task performance. Future research will focus on improving this method without sensitization and expanding the range of real-world operations that can be performed. © 2023 IEEE.

关键词： Virtual reality

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SynBioTrace: Integrating Safety and Security Artifacts to Build Assurance Cases for Synthetic Biology Applications

SynBioTrace: Integrating Safety and Security Artifacts to Bu...

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IEEE International Conference on Software Reliability engineering Workshops (ISSRE Wksp)

作者： Justin Firestone Myra B. Cohen Robyn R. Lutz Dept. of Computer Science & Engineering University of Nebraska–Lincoln Lincoln NE Dept. of Computer Science Iowa State University Ames IA

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

The rapidly advancing cyber-physical domain of synthetic biology modifies the functionality of micro-organisms which can act as living computational devices. Applications include intelligent drug delivery, customized cancer therapies, and pollution detection and mitigation. While many synthetic biology applications have been proposed, prototyped, and even deployed, these systems often lack standard approaches to verify their safety and security. One approach, the assurance case, provides evidence demonstrating proper implementation in the target application, and is often used in other safety-critical domains. However, synthetic biologists lack guidance in developing assurance arguments and tracing safety and security requirements to evidence as required for building assurance cases. Although there has been some research combining safety and security artifacts, such techniques often require extensive expertise from different domains and may not be accessible to synthetic biologists. In this paper we propose SynBioTrace, an assistive process to help propagate information from risk-based analyses of such systems to preliminary assurance cases. SynBioTrace preserves traceability among its steps so that the assurance case can be further refined. We apply and evaluate it through a case study based on a real-world synthetic biology application. Our case study suggests this approach could aid synthetic biologists in identifying, documenting, and structuring safety and security artifacts, as well as linking evidence to support traceability for a complete, integrated assurance case.

关键词： Pollution Prevention and mitigation Conferences Medical treatment Synthetic biology Drug delivery Safety Software reliability Security Standards

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