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LIME Explainability on Flower Classification 6

LIME Explainability on Flower Classification

引用

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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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Shaharear Hossain Emon Iftea Khairul Islam Tasfia Jahan Nahin Ahnaf Mahdin Ahmed Nabiha Tasnim Orchi Md Ashraful Alam Shakib Mahmud Dipto Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science and Engineering CVIS Research Lab Brac University Dhaka Bangladesh Dept. of Computer Science and Engineering University of Liberal Arts Dhaka Bangladesh CVIS Research Lab Brac University Dhaka

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses.

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Early Detection of Black Fungus Using Deep Learning Models for Efficient Medical Diagnosis 4

Early Detection of Black Fungus Using Deep Learning Models f...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Hassan, Esraa Saber, Abeer El-Kenawy, El-Sayed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial intelligence Information technology Dept. Damietta34517 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350387063

Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%. © 2024 IEEE.

关键词： COVID-19

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Blockchain-Based Medical Image Processing Mechanism 4

Blockchain-Based Medical Image Processing Mechanism

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4th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2022

作者： Berwer, Rajiv Kumar Indora, Sanjeev Atal, Dinesh Kumar Deenbandhu Chhotu Ram University of Science and Technology Dept. of Computer Science and Engineering Murthal Sonipat India Deenbandhu Chhotu Ram University of Science and Technology Dept. of Biomedical Engineering Murthal Sonipat India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Blockchain is an emerging technology that provides privacy and security to the user data, ultimately leading to user trust. This paper consists of basic key features of blockchain technology, various kinds of blockchain technology, basic blockchain technology, and the use of a cryptographic function. The blockchain uses a cryptographic hash function that converts an arbitrary size into a fixed-length example: MD5, SHA-256, etc., and proof-of-work to validate the block creation. The blockchain is a decentralized architecture with various applications, such as sharing medical data in healthcare, voting mechanism, personal identity security, supply chain, and cross-border transactions. Blockchain is not limited to only digital currency examples: Bitcoin (BTC), but also has massive applications in the medical field and health sector. The paper's primary purpose is to explore the current work of blockchain in a medical image. Blockchain deals with the privacy and security issues in transmitting, storing or retrieving medical photos. © 2022 IEEE.

关键词： Blockchain

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Unveiling Insights: Vision-Based Data Mining Analysis of Webpages in Transition from HTML 3 to HTML 5

Unveiling Insights: Vision-Based Data Mining Analysis of Web...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Meena Chaudhary Pooja Gunjan Chandwani Dept. of Computer Science and Technology Manav Rachna University Faridabad India Dept. of Computer Science and Engineering Galgotias University Greater Noida India

Deep web material is accessed with web database requests, and returned information is packaged into dynamically generated web pages known as deep Web pages. Due to their intricate structure, it is tedious task to retrieve organized data from deep web pages. A great many approaches to address this problem are yet to be proposed, but all of them have inherent drawbacks because they are based on the website and the programming language. The contents of Sites are always shown for users to search daily, like the common two-dimensional media. This motivates us to search for an alternative way to extract deep Web information, by using some interesting popular visual features from deep Webpages, to address the limitations of past works. A novel blog vision approach is proposed in this paper to overcome these issues. This technique uses visual features in deep web pages specifically to conduct deep web data extraction. This paper also recommends a new appraisal measure to capture the required human activity to make maximum mining. The proposed methodology is highly efficient in the deep network data extraction, according to our studies with a large set of web-based data bases.

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WebRTC-based Resource Offloading in Smart Home Environments

WebRTC-based Resource Offloading in Smart Home Environments

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Jeong, Hunseop Lee, Taehyung Eom, Young Ik Sungkyunkwan University Dept. of Digital Media and Communications Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665441544

Web platforms face new demands for emerging applications, which use machine learning models such as pose recognition or object detection. These models require significant computing powers in processing enormous inputs such as images or audios for machine learning computation. These demands are also being generated in smart home appliances based on web platforms. Unfortunately, smart home appliances do not generally have built-in input devices, such as cameras or microphones, due to privacy issues and have limited performance compared to mobile devices. This paper proposes a WebRTC-based resource offloading system for web applications, which allows smart home appliances to use resources of nearby mobile devices as if the resources are their own. We implemented the proposed system, performed experiments on the resource offloading framework, and evaluated the performance using five computation-intensive web applications, which use a machine learning model. Our system was able to run machine learning models, through resource offloading to mobile devices, on smart home appliances without an attached camera, and achieved up to 1.5x speedup, compared to local execution with a camera. © 2022 IEEE.

关键词： Domestic appliances

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Multilingual Approaches to Named Entity Recognition

Multilingual Approaches to Named Entity Recognition

引用

Delhi Section Conference (DELCON), IEEE

作者： Padmaja Savaram Suhana Tabassum Sasidhar Bandu Lakshitha B Dept. of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering University College of Engineering Osmania University Hyderabad India Dept. of English Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering VNRVJIET JNTU Hyderabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Named Entity Recognition (NER) represents a fundamental operation within Natural Language Processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications.

关键词： Adaptation models Error analysis Transfer learning Collaboration Training data Named entity recognition Data models Multilingual Trajectory Context modeling

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Structural, electronic and optical characterization of edge passivated B and P doped GaN nanostructures 12

Structural, electronic and optical characterization of edge ...

引用

12th International Conference on Electrical and computer engineering, ICECE 2022

作者： Hassan, Md. Abir Subrina, Samia United International University Dept. of Computer Science and Engineering Dhaka1000 Bangladesh Bangladesh University of Engineering and Technology Dept. of Electrical and Electronic Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350398793

In this work, using first principles within the framework of Density-functional theory, we have explored the structural, electronic, and optical properties of two-dimensional edge passivated pristine GaN nanostructures along the zigzag direction. The indirect bandgap of pristine GaN can be tuned by chemical doping and the impurity atom size and doping concentration have a significant impact on the stability of these doped structures. Under both Ga-rich and N-rich circumstances, the effects of boron and phosphorus impurity concentrations have been investigated. The complex dielectric function is significantly modulated for all the structures. There is a noticeable shift in peak position, and lowered peak strength in the spectrum. Due to the newly created impurity states in the forbidden region, additional inter-band transitions are also seen, leading to some discernible peaks in the lower energy range. Based on the influence of chemical doping, it is expected that boron and phosphorus doped GaN nanostructures are potentially suitable for optoelectronic, photocatalyst, visible, and UV detector applications. © 2022 IEEE.

关键词： Monolayers

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review 3

Frameworks, Applications and Challenges in Streaming Big Dat...

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Nabeel Mustafa, Syed Muhammd Umer Farooque, Muhammad Tahir, Muhammad Khan, Shariq Mahmood Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Sir Syed University of Engineering and Karachi Dept. of Software Engineering Pakistan

ISBN: (纸本)9781665461412

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data"refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-Time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-Time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered. © 2022 IEEE.

关键词： Big data

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ADEEC-Multistage Novel Framework For Cattle Identification using Muzzle Prints 2

ADEEC-Multistage Novel Framework For Cattle Identification u...

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2nd International Conference on computer science, engineering and Applications, ICCSEA 2022

作者： Kosana, Vishalteja Gunda, Venkata Sai Praveen Kosana, Vamshikrishna Nit Dept. of Electrical Engineering Andhra Pradesh Tadepalligudem India B. V. Raju Institute of Technology Dept. of Computer Science Telangana Narsapur India Sdes Dept. of Computer Science Telangana Hyderabad India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

This research proposed a deep learning approach using muzzle prints for cattle recognition. This method also proves effective for addressing missing or swapped cattle and false insurance claims. A novel hybrid multistage optimal framework ADEEC (Augmentation-Denoising-Enhancement-Extraction-Classification) for cattle identification and detection is proposed in this paper. The proposed multi-stage framework is developed by hybridizing Denoising Autoencoders (DAE), Super-Resolution Generative Adversarial Network (SRGAN), InceptionResnetV2 feature extractor and multilayer perceptron Network (MLP). The process of denoising the noisy images is carried out using DAE. Then, SRGAN is employed to enhance the denoised images, eliminating the residual noise, making feature extraction more optimal. InceptionResnetV2 model is used to extract meaningful and optimal features from the images and the MLP is adopted to route the classification process. To evaluate the proposed approaches in all stages, different benchmark techniques are used for comparative analysis. The proposed framework performance is assessed through various statistical metrics, and its efficacy is evaluated. With multiple test datasets, proposed framework achieved an accuracy of 98.21%. © 2022 IEEE.

关键词： Feature extraction

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