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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Mishra, Sushruta Obaid, Ahmed J. Alkhafaji, Mohammed Ayad College of Computer Science King Khalid University Abha61421 Saudi Arabia Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar 64001 Iraq

ISBN: (纸本)9789819767250

Brain tumor (BT) segmentation plays a major part in medical image processing. Earlier detection of BTs is an important task in improving treatment possibilities and increasing the patient survival rate. Manual segmentation of BTs for cancer detection, from the abundance of MRI images generated in medical routine, is a time-consuming and difficult task. It is necessary for the automated segmentation of BT images. BT segmentation methods dependent on classical machine learning (ML) and image processing are not ideal sufficient among the existing brain segmentation method. Thus, the DL-based brain segmentation method is more commonly applied. In the BT segmentation technique based on DL, the convolution network technique has a better brain segmentation effect. The deep convolutional network method has the problem of great loss of information and a huge number of parameters in the encoder and decoder processes. With this motivation, this article presents a new Brain Tumor Segmentation using Al‐Biruni Earth Radius Optimizer with Deep Learning (BTS-AERODL) technique on MRI images. The major intention of the BTS-AERODL technique is to segment the affected BT regions in the MRI images. In the presented BTS-AERODL technique, an initial stage of pre-processing takes place in two levels: Non-Local Means (NLM) filter-based noise removal and CLAHE-based contrast enhancement. In addition, the BTS-AERODL technique applies the Inception-CBAM Unet++ (ICUnet++) method for the image segmentation process. Finally, the hyperparameter tuning of the ICUnet++ model takes place by the use of the AERO algorithm. The experimental validation of the BTS-AERODL model is validated on a benchmark brain MRI datasets. The wide results stated the enhanced results of the BTS-AERODL model over other approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective 4th

Analyzing Students’ Emotion and Activities in the Classr...

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4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Konar, Koushik Suman, Shailabh Chaurasia, Diraj Pal, Aniruddha Saha, Sujoy Electronics and Communication Engineering National Institute of Technology Durgapur India Computer Science and Engineering National Institute of Technology Durgapur India Viterbi School of Engineering University of Southern California Los Angeles United States Computer Science and Engineering Bengal College of Engineering and Technology Durgapur India

ISBN: (纸本)9789819726103

Analyzing the concentration levels of students in a classroom and locating their positions pose significant challenges due to varying environmental conditions and diverse features. Facial expressions can reflect an individual’s mood, with different muscle positions beneath the skin indicating various emotions. Head orientation serves as an additional parameter to generalize student concentration levels. Moreover, different body postures can also provide insights into a person’s mood and concentration. This paper presents a lightweight application designed to analyze student concentration levels in a classroom, particularly to support rural education systems. Our work aims to address the challenges of cost-effectiveness and lightweight implementation suitable for running on affordable smartphones commonly used by rural faculty members. We employ the popular Dlib library for face detection, the FaceNet model for feature extraction, and the Siamese network for face recognition. We achieve 95% accuracy in face recognition using publicly available celebrity datasets, and our in-house dataset collected from NIT, Durgapur yields an 88% accuracy. Additionally, we implement MobileNet transfer learning for emotion recognition, focusing on relevant emotions such as Anger, Disgust, Fear, Happy, Neutral, Sad, and Surprise, achieving an accuracy of 73.76%. For Human Activity Recognition, we consider activities such as Clapping, Reading, Raising Hand, Looking Around, and Standing. We employ R-CNN and Random Forest techniques, achieving an accuracy of 64.92% on the in-house NIT, Durgapur dataset. Finally, we develop a mobile app that encompasses all functionalities, including class recording, data synchronization with Internet storage, and registration features for students and volunteers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Research on Woodcut Engraving Image Classification Method Based on Data Augmentation and Improved MobileViT 5

Research on Woodcut Engraving Image Classification Method Ba...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Yang, Xin Zhao, Qijun Xian, Yukai Gao, Dingguo Cairang, Xianmu School of Information Science and Technology Tibetan Information Technology Innovative Talent Cultivation Demonstration Base Tibet University Lhasa China College of Computer Science Sichuan University Cheng Du China School of Information Science and Technology Tibet University Lhasa China

ISBN: (纸本)9798350355925

Tibetan woodcut engraving is a living fossil of Tibetan culture and embodies the wisdom of the Tibetan people. The study of Tibetan woodcut engravings not only helps protect and inherit Tibetan cultural heritage but also promotes the development of related industries and economic prosperity. In this paper, we address the problem of low classification accuracy in traditional algorithms when dealing with the complex textures of Tibetan woodcut engravings. We propose a classification algorithm based on data Augmentation and an improved MobileViT model. Firstly, the long-tail distribution problem of woodcut data is alleviated by data Augmentation techniques, and the classification effect is improved by expanding the dataset with various data Augmentation methods. Secondly, by introducing a coordinate attention mechanism after the MobileViT Block, the model can capture subtle line changes and complex texture details, thereby improving its ability to extract key features from the woodblocks. Given the lack of publicly available datasets, this paper constructs a new dataset of woodcut engraving images and tests the proposed algorithm on it. The experimental results show that compared to the initial MobileViT model, the Fl score of the improved model is increased by 14.74%, enabling quick and accurate classification of woodcut engraving images. © 2024 IEEE.

关键词： attention mechanism data Augmentation image classification Mobile-ViT woodcut engraving

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Research on aerocraft altitude recognition based on Fuzzy SOFM network 2

Research on aerocraft altitude recognition based on Fuzzy SO...

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2nd International Conference on Machine Learning and Intelligent systems Engineering, MLISE 2022

作者： Zhang, JinFeng Southwest University Of Science & Technology College Of Computer Science & Technology Sichuan Mianyang China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

The purposes of this study are to identify the aerocraft altitude. In consideration of the shortcoming of statistics and randomness of influencing factors in aerocraft flight, Fuzzy SOFM model is taken as an effective method to identify the aerocraft altitude. The results show that the method based on Fuzzy SOFM model is effective for recognizing the aerocraft altitude. © 2022 IEEE.

关键词： Training Network topology Neural networks Machine learning Position measurement Numerical simulation Market research

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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MCS-UNet: sea ice classification of Sentinel-1 SAR imagery using a modified U-Net

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Wu, Jianqiang Wang, Changying Qingdao University College of Computer Science & Technology Qingdao China

Sea ice has a significant impact on climate change and ship navigation. Therefore, it is essential to create maps charting that accurately depict the geographic distribution of different types of sea ice. To satisfy the specific classification needs for various types of sea ice and open water during the freezing period, we propose a fully automatic sea ice classification method based on a deep learning semantic segmentation network. By extracting sea ice concentration and enhancing pixel-level semantic segmentation networks, our method achieves high precision and fully automated extraction in segmenting sea ice and open water during the freezing period. We design a multiscale channel-spatial U-shaped network (MCS-UNet). An improved convolutional block attention module is used to extract the subtle differences in texture features and polarization characteristics at different scales. In addition, ResNet modules replace the convolutional blocks in U-Net to ensure the extraction of edge features between different ice types and open water. Experimental results indicate that our method can meet the high-precision classification requirements for open water, gray ice, gray-white ice, thin first-year ice, and medium first-year ice. Compared with the traditional U-Net, the mean intersection over union of MCS-UNet has improved by 5.31%. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Semantic Segmentation

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Person-Independent Hand Gesture Recognition Using MediaPipe and Multi-layer Perceptron

Person-Independent Hand Gesture Recognition Using MediaPipe ...

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International Conference on Smart systems and Wireless Communication, SSWC 2024

作者： Barman, Subhajyoti Majumdar, Sejuti Department of Computer Science and Business Systems Techno India University West Bengal WB Kolkata India Department of Computing and Analytics NSHM College of Management and Technology Durgapur Group of Institutions Durgapur India

ISBN: (纸本)9789819613472

Hand gesture recognition has become increasingly essential, not only for facilitating communication for hearing-impaired individuals but also for implementing automation that minimizes human contact with surfaces, especially in the post-pandemic era. However, the implementation of accurate recognition systems faces several challenges due to variations in human hand size, varying distances from the camera, and hand orientations, making it difficult to establish a direct correspondence between reference gestures and target gestures. To address these challenges, this research employs a machine learning (ML)-based technique that emulates the human brain’s ability to identify gestures in a user-independent manner, regardless of distance, palm size, and orientation. In this research work, the pre-trained MediaPipe hand model has been used to extract hand landmark points from the hand data corpus. These landmark points, along with hand-face position, were utilized to develop feature vectors for the classification process. After normalization, these features exhibited a high correlation within the same category of hand gestures. Initially, a spot-checking activity has been done to recognize the machine learning model that can accurately identify gestures from the normalized feature vectors. The spot-checking process uses eight different machine learning models and they have been fed with a subset of total available hand gestures from the corpus. It has been identified that the multi-layer perceptron (MLP) ensures 100% classification accuracy that motivates the development of the final model using the MLP classification algorithm with 25 different hand gestures. This developed model was able to ensure a 99.62% accuracy level when it was tested with data collected from more numbers of hand profiles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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Floyd-Warshall Algorithm Based on Picture Fuzzy Information

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computer Modeling in Engineering & sciences 2023年第9期136卷 2873-2894页

作者： Shaista Habib Aqsa Majeed Muhammad Akram Mohammed M.Ali Al-Shamiri School of Systems and Technology University of Management and TechnologyLahorePakistan Department of Mathematics University of the PunjabNew CampusLahorePakistan Department of Mathematics Faculty of Science and ArtsMahayl AssirKing Khalid UniversityAbhaSaudi Arabia Department of Mathematics and Computer Faculty of ScienceIbb UniversityIbbYemen

The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of *** works well for crisp weights,but the problem arises when weights are vague and *** us take an example of computer networks,where the chosen path might no longer be appropriate due to rapid changes in network *** optimal path from among all possible courses is chosen in computer networks based on a variety of *** this paper,we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path(APSP)in an uncertain situation of a *** the proposed methodology,multiple criteria and theirmutual associationmay involve the selection of any suitable path between any two node points,and the values of these criteria may change due to an uncertain *** use trapezoidal picture fuzzy addition,score,and accuracy functions to find *** compute the time complexity of this algorithm and contrast it with the traditional Floyd-Warshall algorithm and fuzzy Floyd-Warshall algorithm.

关键词： Trapezoidal picture fuzzy number score function accuracy function shortest path problem Floyd-Warshall algorithm

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Design of XSRP-based automatic identification system for Analog modulated signals 3

Design of XSRP-based automatic identification system for Ana...

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Xu, Jiayue Ni, Ming Zhu, Danjiang Ding, Xiyao East China Institute of Computer Technology Shanghai201800 China College of Intelligence Science and Technology National University of Defense Technology Changsha410000 China

ISBN: (纸本)9781510668867

The automatic identification of the modulation mode of the communication signal is the main task of the intelligent receiver, which is now widely used in a variety of fields, including civil and military. In this paper, an XSRP-based analog modulated signal automatic identification system is designed, and the overall program block diagram is drawn using LabVIEW software. The design of the automatic identification system is divided into seven modules, focusing on the following two modules: the first is the extraction of each feature parameter when the modulated signals of AM, DSB, SSB and FM are subdivided layer by layer, and the second is the simulation of modulation mode prediction using the library function of MATLAB BP neural network algorithm. Simulations were carried out in conjunction with the XSRP software radio platform, with suitable parameters and sample sizes selected for validation, and improvements made to feature parameters to improve system performance. computer simulations show that the XSRP-based analogue modulated signals are eventually identified correctly at no less than 95% and can be expected to be used for the detection and fast identification of signals in practical non-collaborative communication systems. © 2023 SPIE.

关键词： MATLAB

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