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1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Pangsatabam, Hoomexsun Chanu, Yambem Jina Singh, Naorem Karline Department of Computer Science and Engineering National Institute of Technology Manipur Manipur Imphal795004 India

ISBN: (纸本)9789819947126

The paper focuses on Manipuri, an under-resourced language, and is written in a way that will help eager researchers build a robust speech processing system. To improve the quality of speech processing and language processing systems, the tools and models need to be properly designed and evaluated. Various metrics are considered in the areas of syllabification, speech recognition, speech synthesis, machine translation, etc. Multiple works have demonstrated that authors value accuracy, precision, and scores. Tests are designed to assess data perplexity, naturalness of speech, and intelligibility. Furthermore, the challenges and difficulties associated with the development of various systems are compared. The evaluation metrics, both subjective and objective, are thoroughly discussed, as are various modifications designed to address specific issues arising during data collection, design, and evaluation, mainly in speech recognition and speech synthesis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech synthesis

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Global Context-Aware Method for Infrared Image Non-Uniformity Correction 3

Global Context-Aware Method for Infrared Image Non-Uniformit...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Ma, Zhiwei Zhang, Shuiping Wang, Lei Rao, Qiang School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Infrared imaging systems are often affected by non-uniformity, which can significantly reduce the quality of captured images and hinder subsequent image-processing tasks. Existing infrared image non-uniformity correction methods mainly suffer from incomplete detail recovery and easy ghosting. This paper proposes a global context-aware infrared image non-uniformity correction method, which effectively captures long-distance dependencies and complex feature patterns by combining residual networks and self-attention mechanisms. To verify the effectiveness of our method, we conducted extensive experiments on multiple public datasets. Experimental results show that compared with the existing state-of-the-art technology, our method performs better in both qualitative and quantitative evaluations and can effectively remove infrared image stripe noise in complex scenes while retaining key details of the image. © 2024 IEEE.

关键词： Infrared imaging

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Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

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computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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CBCD: Comprehensive Analysis for Bone Cancer Diagnosis through MRI Imaging 1

CBCD: Comprehensive Analysis for Bone Cancer Diagnosis throu...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Srinivasu, Parvathaneni Naga Sravya, Valluri Sri Tagore, Vusala Deepak Vinoothna, Veedhi Mokshada, Yakkala Prasad V Potluri Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350361780

Bone cancer is an epidemic of the skeletal system that is impacted by a complex interaction of genetic variables and inherited susceptibility. It is critical to develop a readily accessible detection and classification method for early detection of this cancer. As a consequence, a computerized procedure for distinguishing between cancerous and healthy bones is necessary. This paper is dedicated to developing a system that uses multiple magnetic resonance imaging (MRI) scans in digital imaging and communications (DICOM) format of cancerous and healthy bones that exhibit comparable structural traits from different individuals to identify and classify bone cancer using convolutional neural networks (CNN). The proposed approach can be broken down into three parts: MR image improvement, feature encoding, and categorizing. After preprocessing an image, the features are retrieved using discrete wavelet transformation (DWT). Finally, neural networks (NN) differentiate between normal and diseased bone. This method hybridizes one of the most efficient CNN architectures, Visual Geometry Group (VGG) 19. An effective strategy for cancer detection is proposed based on spatial fuzzy C-means (SFCM) clustering. A rigorous performance evaluation showed that the proposed CBCD method is extremely successful in detecting bone cancer, with an amazing accuracy rate of 92.9%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Smart Intelligence-Driven Deep Learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial Intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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IoT Based Drip Infusion Monitoring System

IoT Based Drip Infusion Monitoring System

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Maniktalia, Riddhi Tanwar, Satwik Billa, Ritvik Deepa, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9781665458832

During recent years, we have seen many technological advancements which help to take better care of patient's health and assure them fast and safe recovery. The most basic item necessary is competent patient care in hospitals, as well as proper management of fluid and electrolytes. In hospital almost all patients, mainly ICU patients, have to regulate the volume of fluids and electrolyte into the bloodstream 247 which is done using drip. These drips need regular monitoring or changing to maintain constant flow of fluids or to prevent any infection to patients. Though this system is simple, overcrowded hospitals and the risk of deficiency of nurses can put patients at health risk which can deteriorate patients' health more aggressively or in worse case it can lead to death. Almost in all hospitals, nurses or the hospital staff are responsible for monitoring the drip level. But unfortunately, because of their hectic schedule, the observer may forget to change the bottle at the appropriate time. During the pandemic, hospitals were overrun with patients, and nurses were unable to do manual regular checks on the drip conditions and drip level of every patient, even after working extra shifts. Many patients even died due to not being able to get proper care from nurses. The next step in providing more effective and easy healthcare is to automate such vital procedures. To overcome this critical situation, we are proposing an IoT (Internet of Things) based Drip Monitor System using Arduino UNO which eases the process of measuring and solves the issues of bubble formation in drips. © 2022 IEEE.

关键词： Health risks

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Research on YOLO model optimization of multi-modal data processing attention mechanism based on deep learning 4

Research on YOLO model optimization of multi-modal data proc...

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4th IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2024

作者： Wu, Qiongfei Xie, Qubo Wuhan Institute of Design and Science School of Intelligent Engineering Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350361643

In this study, a YOLO model method combining LightGBM and multi-modal data processing attention mechanism optimization is proposed to enhance the effectiveness and accuracy of object detection. The traditional YOLO model has the problem of insufficient utilization of multi-modal data when processing image data, while LightGBM has significant advantages in processing structured data. Therefore, this paper designs and implements an optimization framework by embedding CBAM module into the extraction of features part of YOLOv5 model to improve the capability of feature representation. Concurrently, LightGBM is employed to handle structured data, and a fusion module is used to combine the features from both sources with appropriate weighting. © 2024 IEEE.

关键词： Object detection

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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From Ranch to Worldwide:Observing Soil Welbeing Patterns

From Ranch to Worldwide:Observing Soil Welbeing Patterns

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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Divakar, K.M. Ankith Gowrishankar Esturi, R. Anil, P.S. Namith, D. Chethan, A. S J C Institute of Technology Dept. of Computer Science Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Declining soil wellbeing represents a huge danger to worldwide food security and rural maintainability. Precisely checking soil wellbeing patterns and anticipating future land appropriateness at nearby and worldwide scales are essential for illuminating feasible administration practices and strategy intercessions. This paper proposes a clever structure that use the force of large information investigation to address this test. We expect to overcome any issues between ranch level checking and worldwide experiences, engaging partners at each level to come to informed conclusions about land use and management. Our structure depends on the mix of assorted information sources. High-goal remote detecting information, including satellite symbolism and aeronautical photography, will give basic experiences into soil properties, crop conditions, and land the board rehearses. We will additionally improve the examination by consolidating promptly accessible open-access datasets on environment, soil overviews, and financial data. This exhaustive information combination approach permits us to catch the diverse idea of soil wellbeing and land reasonableness dynamics. Extracting significant experiences from these tremendous datasets requires clever examination devices. We will use a setup of AI procedures, including regulated and solo learning calculations, to reveal stowed away examples and connections inside the information. Directed learning will empower us to foster prescient models for soil wellbeing appraisal and land appropriateness planning, while solo learning will assist with recognizing remarkable attributes and likely peculiarities in various farming *** system will create constant soil wellbeing maps, featuring areas of corruption, supplement lacks, and potential improvement open doors. This enables ranchers to come to informed conclusions about manure application, water the board, and harvest choice, advancing their activities for expanded yield and long ha

关键词： Fertilizers

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An Optimized TF-IDF Features based Deep Learning Enabled Automated Fake News Detection Scheme

An Optimized TF-IDF Features based Deep Learning Enabled Aut...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Aggarwal, Nikhil Kumar, Siddharth Chandigarh University Apex Institute of Technology Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350361537

People are exposed to a variety of false news and disinformation because of the continuously changing social media landscape and the various sources of information used by news organizations and production companies. We are now conducting research to find useful techniques to detect this false news, and the designed system is known as deep learning enabled an automated Fake News Detection (FND) system. The categorization or identification of fake news in online social media platforms has drawn more interest from the machine learning community. Misleading news refers to news stories that purposefully mislead readers by disseminating false information. In this post, we suggest a solution for social media platforms that automatically detects bogus news. Pre-processing, extraction of features, feature optimization, and classification were the four processes employed by the suggested system. Here, the concept of Term Frequency-Inverted Document Frequency (TF-IDF)-based approach is used as the feature extraction scheme and the extracted features are optimized using the concept of swarm-based Artificial Bee Colony (ABC) by designing an appropriate and novel fitness/objective function. ABC creates two categories of news, first as Fake and second as Real. Based on these categories, further Convolutional Neural Network (CNN) is used to train the FND model. To verify the effectiveness of the suggested FND model, parameters like Precision, Recall, and F-measure (PRF) with Accuracy are computed. Finally, I assess how the suggested FND system performs in comparison to current models. to validate the performance in terms of PRF and we observed that the FND system got better results with accuracy of 99% for most of the cases. © 2024 IEEE.

关键词： Convolutional neural networks

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