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Multi-label and Multi-class Classification on a Custom Dataset using Convolution Neural Networks 7

Multi-label and Multi-class Classification on a Custom Datas...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Bonthu, Sridevi Kiran, Kompella Bhargava Deenakonda, Mamatha Rao, V.V.R. Maheswara Jagadeesh, S.V.V.D. Vishnu Institute of Technology Computer Science and Engineering Bhimavaram India Vishnu Institute of Technology Electrical and Electronics Enigneering Bhimavaram India Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9798350397253

A multi-label image classification is a challenging task as it has to map an input image to a vector of outputs. This work presents a single and efficient model to perform multi-label and multi-class classification using Convolutional Neural Networks (CNN). A custom dataset is collected and annotated with eight labels to test the neural network. The classes for the labels in the dataset are a blend of balanced and unbalanced data. The proposed network is a two-stage network. The first stage is used for general training, whereas the second stage is used for specific training. The results show that the usage of a single model across multiple labels results in varying performance. The number of classes and the balance in the class distribution have a direct impact on the performance of the model. The code is available at https://***/sridevibonthu/FML. © 2023 IEEE.

关键词： Convolutional neural networks

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Brain Inspired Cognitive Architecture of Hierarchical Distributed Model Based on Artificial Intelligence

Brain Inspired Cognitive Architecture of Hierarchical Distri...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Lafta, Alaa M. Hameed, Waleed Hussain, Angham Khalid Taha, Taha Abdulsalam Fouad, Laith Noori, Mohammed Alabdeli, Haider National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

The next generation of Artificial Intelligence (AI) will need to engage in social interactions with its users and encourage them that it can comprehend mental and emotional states. Several cutting-edge cognitive architectures have recently taken inspiration from the human brain, with researchers analyzing and contrasting them based on how they model the brain's neuronal mechanisms for feeling. This work classifies the Brain-inspired cognitive architecture (BICA) using Artificial Intelligence termed the Hierarchical Distributed Model (AI-HDM) for the computation of the human brain to determine novel knowledge based on the inputs sensed by sensory systems acquired from physical environments. The data are taken from the Human Memory and Cognition Kaggle Dataset. To understand the internal functioning and dynamic nature of the brain's neural processes, a semantic map is used to encompass a foundational set of motor abilities, perceptions, and emotions. Different facets of human behavior may be self-organized according to the patterns of neuronal activity seen during complicated decision-making processes. Both empirical and simulated results demonstrate that the suggested method may significantly influence BICA and outperforms more traditional frameworks in prediction accuracy. The experimental outcome demonstrates that the suggested Al-HDM model increases the efficiency ratio by 92.4%, understanding ability by 93.5%, active learning by 94.4% and reduced error rate of 9.6% compared to other existing models. © 2024 IEEE.

关键词： Semantics

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Observing and Predicting Land Slide using Three Stage Warning System in WSN 4

Observing and Predicting Land Slide using Three Stage Warnin...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Akila, T. Vasanth, P. Kalaiselvi, S. Nalini, M. Mahendra College of Engineering Department of Information Technology Tamil Nadu Salem India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Kongu Engineering College Department of Computer Technology Tamil Nadu Erode India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

A landslide is an environmental disaster that originates from humans' enormous destruction of infrastructure. Real-time observing of a landslide lying region is essential to the problem for warning. A wireless sensor network (WSN) is an effective technique for observing real-time disaster-prone areas. To achieve real-time observation, an enormous amount of information must be gathered and examined in a short time. This approach has been introduced for efficient information gathering by the threshold warning stage. This approach proposes Observing and Predicting Land Slides (OPLS) using three Stage Warning System in WSN. Apply a three-stage warning system that decides the amount of information gathered and prioritizes the average and high-level warning message. The initial stage warning stage is a low-level alert, and the average stage warning system contains a medium-level warning system. In addition, the high-stage warning system represents the high-level alert message. Use sleep and awake scheduling to minimize the network's energy consumption in this approach. It is an important message, and this message evades high-level destruction. The queue will prioritize the high-level alert message to avoid the time delay. The experimental outcomes demonstrate that the OPLS method minimizes the packet loss ratio, and the sensor node provides better sensing results in better accuracy in the WSN. © 2023 IEEE.

关键词： Energy utilization

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Review the Amazon Products Using Data science Tools to Enhance Online Feedback and Services 2

Review the Amazon Products Using Data Science Tools to Enhan...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Elhussein, Bahaeldein Karrar, Abdelrahman Elsharif College of Computing and Information Technology University of Bisha Bisha Saudi Arabia College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9798350305418

Many websites provide their services officially and unofficially, leading to many customer problems, such as fraud, delays in the services offered, and many other issues. Millions of consumers are victims of Scams every year—online fraud daily in different ways. The research-collected data set from the Kaggle Platform contains different products from the Amazon website and is analyzed. A quantitative Descriptive Method followed the research—a list of over 3,400 more client reviews for Amazon products. The Kindle, hearth TV Stick, and additionally provided by Datafiniti's Product info. The dataset includes every product's basic product info, rating, review text, and additional information. It has introduced a model to obtain reviews from online users. Finally, it was paying more attention to evaluating and developing services because, unfortunately, many institutions use them unprofessional to bring them profits. However, with the idea of introducing a platform specializing in evaluation processes for the various services provided by multiple institutions and its further development in the future, it becomes challenging to manipulate the consumer and society. Service evaluation has become very important, especially these days, so adding features that greatly help avoid fraud, deception, and manipulation is a good idea. Unfortunately, evaluation has become insufficient to obtain the service effectively, so it is necessary to develop a practical Platform work, especially in this field in the future, effectively helping the community. © 2023 IEEE.

关键词： Data sciences Tools Evolution Services Fake Reviews Online Scams Rating System

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Convolution Neural Network for Gastrointestinal Cancer Detection and Classification using Deep Learning

Convolution Neural Network for Gastrointestinal Cancer Detec...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sathishkumar, R. Nirmalraj, M. Govindarajan, M. Jaisree, J. Haripriya, L. Santhiya, M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Women's Engineering College Department of Computer Science and Engineering Puducherry India Annamalai University Department of Computer Science and Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350315127

Ailments of the gastrointestinal tract (GIT), including bleeding, ulcers, polyps, Crohn's disease, and cancer, are becoming more prevalent. Ulcers and bleeding in the small and large bowels are two of them that are particularly common. Medical experts find the manual diagnostic process to be both time-consuming and difficult. As a result, experts have suggested computerised techniques for the identification and classification of certain disorders. Medical video endoscopy produces a huge volume of images, thus it takes a long time for specialists to look over them all. Early detection and diagnosis of certain illnesses can result in successful therapy. SqueezeNet, ResNet-101, and DenseNet-169 are three deep learning-based models that we introduce in this paper and assess for their capacity to diagnose a dataset of lower gastrointestinal disorders. There are 5,000 images in the Kvasir dataset, similarly distributed among five different lower gastrointestinal conditions, including ulcerative colitis, polyps, normal cecum, and normal pylorus. The deep feature vector is processed using the softmax activation function, which divides the input images into five groups. Surprisingly, all of the convolutional neural network (CNN) models had exceptional performance, with DenseNet-169 reaching accuracy, specificity, precision, recall, and F1 Score values of 97.8%, 98.07%, 97.5%, 96.02% and 97.6%. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep Learning Framework for Early Detection of Heart Attack Risk and Cardiovascular Conditions using Retinal Images 1

Deep Learning Framework for Early Detection of Heart Attack ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sakthidevi, I. Srinivasan, A. Gayathri, C. Julie, E. Golden Krishnan, R. Santhana Darney, P. Ebby Adhiyamaan College of Engineering Department of Information Technology Hosur India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350359756

In today's world, cardiovascular diseases including heart attacks are the prime reason for mortality. Detecting heart attack risk at early stages and accurate assessment of such risks related to cardiovascular conditions play an essential role in active patient management and preventive interventions. In the current research work, a novel deep learning framework called "CardioSightFrame"is proposed for detecting heart attack risks and cardiovascular conditions at early stages using retinal images. The proposed algorithm integrates Graph Convolutional Networks (GCNs) and Vision Transformers (ViTs) to control both local structural information and global contextual understanding from the retinal scans. A comprehensive simulation analysis is conducted to evaluate the performance of CardioSightFrame and it is compared with the existing algorithms. Also, the predictive accuracy of the proposed algorithm is measured using appropriate simulation metrics. By treating retinal images as graph nodes and leveraging self-attention mechanisms, the proposed algorithm achieves accurate and efficient feature extraction which is vital for early detection of heart attack risk and cardiovascular conditions. To evaluate CardioSightFrame's performance, wide-ranging simulation experiments are conducted using a varied dataset of retinal images. Through these comparisons, the predictive accuracy, sensitivity, specificity, and computational efficiency of CardioSightFrame are established. The simulation results indicate that CardioSightFrame outclasses existing algorithms in terms of accuracy and sensitivity for early detection of heart attack risk and cardiovascular conditions. The algorithm's ability to efficiently investigate retinal images with reduced computational overhead further enhances its applied pertinence. © 2023 IEEE.

关键词： Image enhancement

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Detection of Medicinal Plants Using Machine Learning 1

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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C2F-CCPE: Coarse-to-Fine Cross-View Camera Pose Estimation

C2F-CCPE: Coarse-to-Fine Cross-View Camera Pose Estimation

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yong Tang Qiang Huang Yingying Zhu College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Image-based cross-view localization often yields imprecise camera pose estimations due to the limited sampling density in the satellite image database. Current cross-view camera pose estimation approaches often overlook the importance of multi-scale features. In this paper, we introduce a novel coarse-to-fine cross-view camera pose estimation method (C2F-CCPE) that leverages multi-scale feature fusion and a localization and orientation feature fusion module (LOFFM) to enhance performance in localization and directional prediction. The proposed method outperforms state-of-the-art approaches on two benchmark datasets, addressing limitations in existing methods that focus on single-scale feature representations. And C2F-CCPE captures global and local information simultaneously, improving robustness against occlusions and enhancing precision in complex scenes. LOFFM further aggregates directional and positional information, enabling the network to deeply comprehend location features. Quantitative and qualitative experiments on two datasets not only demonstrate that our proposed method outperforms state-of-the-art models but also verify the effectiveness of our proposed method.

关键词： Location awareness Accuracy Databases Aggregates Pose estimation Benchmark testing Cameras Robustness Satellite images

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Artificial Intelligence-Assisted Pattern Discovery for Narrow Domains of Human Activity Using a Heuristic-Optimised Approach

Artificial Intelligence-Assisted Pattern Discovery for Narro...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Mohsin, Sarah Kadhim Kadeem, Sahar R. Abdul Sheel, Shaid Hamid, Noor Kaylan Fahidhil, Eay Noori, Mohammed Hussein, Laith Al-Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

This work outlined the process of creating automatic recognition systems for analysing human behaviour. Here, heterogeneous sensors are implanted in the human body to record data in real-time, enabling monitoring of a wide range of physiological signals in response to the subject's behaviour. In addition, this study aimed to examine the ages and daily routines of people with physical disabilities who rely on ambient assisted living technology. This study analyses a state-of-the-art Human Activity detection (HAR) approach by analysing signals collected from real-world activity detection applications. For activity recognition, this research employs a deep belief network trained in an unsupervised layered manner on statistical features extracted from multi-sensor a smartphone data. Data from the human sensors are gathered via high-tech mobile devices and then analysed by a robust deep-learning network. The experimental study shows that several standard approaches have been used to analyse the collected data based on the HAR pattern at lab size. The suggested technique is evaluated using the HAR datasets, and the findings demonstrate that it has a higher accuracy (98.67%) than the standard system. © 2024 IEEE.

关键词： Active learning

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God’s Eye: An Assistive Device for Blind

God’s Eye: An Assistive Device for Blind

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NIELIT's International Conference on Communication, Electronics and Digital technology, NICE-DT 2023

作者： Mishra, Apoorva Prajapati, Nishant Srivastav, Yash Shukla, Pankaj Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering Rameshwaram Institute of Technology and Management Lucknow India Lucknow India

ISBN: (纸本)9789819916986

Through the advancement of multidisciplinary subjects such as the Internet of Things, computer science and electronics, and hardware engineering, a God’s Eye for the blind allows visually impaired individuals to explore at speed and confidence by recognizing objects and people nearby deterrents using ultrasonic waves and informing them with audio assistance and beeping sounds. According to the World Health Organization, an estimated 39 million people are blind worldwide. Our goal is to design a cheap and more efficient product that will greatly benefit visually impaired individuals and people who depend heavily on others. The Arduino Pro Mini/Nano board has an ultrasonic sensor, composed of a module, tied like a band. Through the use of this sensor module, the person can see objects around them and can travel efficiently. Whenever this sensor detects an object, it will emit a beep sound or vibration to notify the person. They just need to wear this device as a band or cloth. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer hardware

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