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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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Combining Historical Data with Future Predictions to Assess Urban Noise and air Quality

Combining Historical Data with Future Predictions to Assess ...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Veeram Venkata Siva Prasad K Bhanu Chand R Uma Aruna Devi Jonnalagadda Krishna Badde Srinivasarao Computer Science And Engineering Sai Spurthi Institute of Technology India Department of Information Technology Swarnandhra College of Engineering and Technology India Department of CSE(AI&ML) Sai Spurthi Institute of Technology India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Analysing Urban Noise and air Quality by Integrating Past Data Accompanying Projections for the Future. By joining predicting models accompanying Internet of Things technology, this study tackles the question of judging buzz and air kind in urban regions. To do this, a model was used to devise noise and PM2.5 heat maps. Open-beginning traffic data was again used for correct contextualization. Using this form, find the districts with extreme levels of contamination and crashes are likely situated on busy roads and technical zones. R2 principles of 0.93 for PM2.5 and 0.90 for blast showed that the forecasting models, which involve conclusion trees and convolutional affecting animate nerve organ networks, were completely correct in guessing the levels of dirtiness and crash. These findings demonstrate the significance of actively seeking out methods or policies that have no adverse impact on environmental issues in reports when formulating municipal plans. Furthermore, the study proposes strategies to enhance the city's conservation of natural resources, drawing on the conclusive findings. These procedures contain the exercise of reduced-issuance zones and the promotion of green fields. A joined and data-compelled plan is important for compensating policy selections in city preservation of natural resources, and this study creates a solid gift to both scientific knowledge and useful use in these fields.

关键词： Natural resources Atmospheric modeling Roads Noise Urban areas Transportation Predictive models Solids computer crashes Heat maps

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer

Emotion Recognition in Visual Art on Color and Texture Analy...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： K G Suma Zaed Balasm N. Sasirekha Veera Manikandan P K. Sandhya Department of AI & ML School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual–Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively.

关键词： Emotion recognition Visualization computer vision Art Accuracy Image color analysis Face recognition Speech recognition Feature extraction Transformers

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ABSPYRAMID: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

ABSPYRAMID: Benchmarking the Abstraction Ability of Language...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Wang, Zhaowei Shi, Haochen Wang, Weiqi Fang, Tianqing Zhang, Hongming Choi, Sehyun Liu, Xin Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong Tencent AI Lab Bellevue United States

ISBN: (纸本)9798891761193

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present ABSPYRAMID, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, ABSPYRAMID collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ml) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ml algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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A Novel Approach to Optimizing Spark: Detecting Misconfigurations for Enhanced Efficiency

A Novel Approach to Optimizing Spark: Detecting Misconfigura...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Sanjana S V Rachana Uday Umesh Suraj Rao R. Jayashree Department of Computer Science and Engineering PES University Bengaluru India Department of Computer Science and Engineering (AI & ML) PES University Bengaluru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Configuration errors constitute 60% of contemporary system failures in distributed computing platforms. These errors lead to data breaches, system crashes, or silent failures requiring extensive technical support and knowledge of the application architecture. This research work introduces a dataset that can be used to detect misconfigurations in a Spark cluster. The dataset encompasses detailed system performance metrics such as CPU usage and memory allocation that were recorded by running CPU intensive and memory intensive Spark jobs under various configuration settings to capture performance variations. Analysis of this dataset provides insights into optimizing the values of Spark cluster configurations and mitigating the impact of configuration errors. By using machine learning techniques on this dataset, it is possible to construct automated methods for proactively detecting and mitigating misconfigurations, which improves the stability and efficiency of distributed computing environments.

关键词： Measurement System performance Machine learning Data breach Stability analysis computer crashes Sparks Resource management Distributed computing Intelligent systems

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Enhancing Real-Time Stream Processing with Apache Kafka by integrating Scaled-Hidden Markov Models

Enhancing Real-Time Stream Processing with Apache Kafka by i...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： V Rachana Sanjana S R Jayashree Department of Computer Science and Engineering PES University Bangalore India Department of Computer Science and Engineering(AI & ML) PES University Bangalore India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the integration of Kafka, a distributed streaming platform, in the context of enhancing speech recognition systems utilizing Hidden Markov Models (HMMs). Speech recognition is a vital aspect of ai and Machine Learning which plays an important role in the interaction between humans and computers. Hidden Markov models (HMMs) are a popular choice for speech recognition systems due to sequential data modeling. In this paper, we conduct a comprehensive study integrating Big Data technologies like Kafka into Hidden Markov Model (HMM) based speech recognition, aiming to recognize spoken words from audio wave files and classify them into predefined word classes. The integration involves a multifaceted approach, employing Kafka for efficient data processing and communication between distinct system modules. To prevent underflow during HMM training, a Scaled HMM Module is employed. We address challenges posed by unseen data, by incorporating smoothing techniques such as add-delta smoothing which mitigates zero probabilities and improves the generalization of the model. We also experiment the use of pruning techniques which disregards the low-probability paths during the Viterbi algorithm, which results in improvement in the accuracy. By incorporating the integration of Big Data technologies like Kafka, along with advanced techniques such as pruning, smoothing, and scaling of Hidden Markov Models (HMMs), we delve into a comprehensive strategy for advancing speech recognition capabilities.

关键词： Adaptation models Analytical models Smoothing methods Accuracy Viterbi algorithm Hidden Markov models Speech recognition Big Data Data models Robustness

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Enhancing the Efficiency of Convolutional Neural Networks through Quantization 4

Enhancing the Efficiency of Convolutional Neural Networks th...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kansal, Vipashi Shnain, Ammar Hameed Pushkarna, Gaurav Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Graphic Era Deemed to be University Department of Computer Science & Engg Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engg Najaf Iraq Lovely Professional University Phagwara India New Horizon College of Engg Department of Mechanical Engg Bangalore India Gla University Department of Electrical Engg Mathura India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331518981

A new kind of image categorisation technology, Convolutional Neural Networks (CNNs) have shown themselves capable of astounding accuracy across a range of uses. Problems arise, however, when dealing with real-time applications in settings where resources are limited due to their computational complexity and resource needs. An extensive investigation on how to make convolutional neural networks (CNNs) better at picture classification is detailed in this work. We explore several optimization techniques, including network pruning, quantization, and the deployment of lightweight architectures such as MobileNet and SqueezeNet. Additionally, we investigate the impact of advanced training strategies like transfer learning and data augmentation on model performance. We show, by means of comprehensive tests, that our suggested approaches considerably reduce the memory footprint and computational cost of CNNs while preserving or even enhancing classification accuracy. Our findings provide valuable insights for deploying efficient CNN models in practical scenarios, paving the way for more accessible and scalable image classification solutions. © 2024 IEEE.

关键词： Convolutional neural networks

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