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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Srivastava, Saurabh Ahmed, Tasneem Kumar, Rajeev Moradabad Institute of Technology Department of Computer Science & Engineering Moradabad India Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350379716

Accurate monitoring and assessment are necessary due to the enormous challenges posed by rapid urbanization to sustainable development and resource management. This study utilizes Sentinel-2 satellite images of Moradabad City, located in Uttar Pradesh, India, to analyze urban growth through a machine learning approach. Sentinel-2 imagery is widely used for land cover monitoring, and images from 2017 and 2023 were selected to assess the rapid expansion of urbanization. Various techniques, including SVM, ANN and minimum distance (MD), were employed for land surface classification. Among these, SVM produced the most reliable results for estimating urban areas, based on both the Kappa coefficient (Kc) and overall accuracy (O.A.). The SVM estimates that in 2017 about 8.308% of the region was classified as an urban area, and from 2023 that number had increased to about 11.64%, which could be a serious concern for Moradabad city's residents. The motivation behind this study to explore the potential applications of machine learning methods for small-scale urban dynamics monitoring and analysis through satellite image analysis. The research advances knowledge of urbanization processes and offers insightful advice for sustainable urban growth in quickly expanding places. © 2024 IEEE.

关键词： ANN MD Sentinel-2 SVM Urban Area Estimation Urban Growth estimation

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Activity Recognition System via Unification of CNN and SVM in Complex Domain 6

Activity Recognition System via Unification of CNN and SVM i...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Choudhary, Parul Pathak, Pooja Chaubey, Abhishek GLA University Department of Computer Application and Engineering Mathura India

ISBN: (纸本)9798350346961

Human automatic activity recognition is an essential part of a little interactive application which involves human-being. The main disadvantage is the person's diverse activities. These techniques provide high accuracy and pattern recognition. Deep learning methods succeed in human activity recognition (HAR);hence our goal is to combine Convolutional Neural Networks (CNN), which work with image data and extract features from images with Support Vector Machines (SVM), a shallow architecture that maps low-dimensional space to high-dimensional space. This paper uses an intelligent system algorithm to detect human activity and recognize its pattern. Cooking, cleaning, dancing, steering, and discussion activities are to be tested. Each class has 7480 images. Applying all preprocessing techniques, experimental results shows that the cleaning class gives the highest training and testing accuracy i.e., 98.78% and 97.51%. This method achieves the highest recognition accuracy with the lowest computational cost.. © 2023 IEEE.

关键词： Support vector machines

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An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid 11

An Analysis Conducted Retrospectively on the Use: Artificial...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kaushik, Ruchi Sharma, Kanta Prasad Malik, Sidharth Shanbhog, Manjula Delhi Ncr Campus Department of Computer Science Ghaziabad India Gla University Department of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350350357

The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. © 2024 IEEE.

关键词： Ultrasonography

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Registration of CT Images of Lung Using Fireworks Algorithm 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Nayak, Somen Mondal, Subhodip Obaid, Ahmed J. Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Mathematics Department of Computer Science University of Kufa Najaf Iraq

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

The acquisition of medical images from various modalities can provide a number of practical challenges that can be addressed through medical image registration. Intensity-based registration methods, which are used in multi-modal registration, merge multiple images with the same content into a single representation by applying image transformation. It is essential to edit photographs as best as you can. It is highly important to improve the similarity measure between the various photographs. We introduce a new method for registering lung’s CT image affected with COVID-19 in this work since the image registration process is fundamentally an improvement problem. Lung computed tomography image registration is a technique for aligning pre- and post-contrast images for analysis the effect of COVID-19 virus in the lung. A meta-heuristics-based optimization technique called FWA-IATF was explored for registering lung CT images. Following that, the FWA-IATF algorithm successfully registers the images. The results of registration based on the FWA-IATF method and PSO approach are compared. The results indicate that the FWA-IATF-based registration methods outperform the PSO-based registration approach while registering lung CT images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Detecting and recognizing faces from video images using multi-deep CNN based rank-level fusion 2

Detecting and recognizing faces from video images using mult...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

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Unsupervised Bearing Fault Diagnosis Method for Complex Electromechanical Hydraulic System 4

Unsupervised Bearing Fault Diagnosis Method for Complex Elec...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： He, Zizhou Shen, Wenwen Li, Suicheng Yuan, Tianyou Zhou, Xiaowei Ni, Zhigao Institute of Computer Application CAEP Sichuan Province Department of Computer Engineering and Science China

ISBN: (纸本)9798350324303

Finding the optimal combination of feature extraction and classifier has always been a hot issue in intelligent health monitoring. Traditional artificial features are difficult to achieve the ability of deep learning to mine deep abstract features of signals. However, compared with ensemble learning models, deep learning classifiers have the problems of easy overfitting, poor generalization performance, and the need for a large number of labeled samples. Autoencoder has the advantage of unsupervised feature extraction, LightGBM has the advantage of feature selection, so this paper proposes a fault diagnosis method combining deep convolutional sparse autoencoder with LightGBM, which has the advantages of unsupervised feature extraction and distributed gradient boosting. Experiments are carried out on the CWRU bearing dataset to verify the superiority of the proposed method. © 2023 IEEE.

关键词： Feature extraction

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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Applying Bayesian belief in LoRa: smart parking case study

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7857-7870页

作者： Gupta, Sakshi Snigdh, Itu School of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

With the popularity of LoRa, many IoT applications are now being realized with this Low Power Wide Area Network (LPWAN) technology. Apart from ensuring low power and long-range communications, LoRa is also interference-free and hence an obvious choice for applications in urban scenarios. We dedicate our study to analyze and infer the impact of LoRa parameters like SF (Spreading Factor), BW (Bandwidth), CR (Coding Rate) on network performance metrics like delay, throughput for a smart parking scenario. Suited to the smart parking case study, we also present network performance observations for varied LOS settings (Line-of-Sight), NON-LOS, and obstruction. We try to identify the most significant parameters under different network conditions and determine the relationship among them through canonical correlation through our article. We also propose a Bayesian belief network to infer the best possible combination of LoRa parameters for achieving high-quality network efficiency under the environment scenarios. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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Sentiment Analysis for Movie Reviews on Social Media based on Sentiment Lexicon and Deep Learning 15

Sentiment Analysis for Movie Reviews on Social Media based o...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jakhar, Yogesh Kumar Mishra, Nidhi Department of Computer Application University of Engineering & Management Jaipur India Amity University Noida India

ISBN: (纸本)9798331300579

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User's review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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