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AI-powered Lung Cancer Diagnosis: the Potential of Artificial Neural Networks

AI-powered Lung Cancer Diagnosis: The Potential of Artificia...

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Innovation in Technology (ASIANCON), Asian conference on

作者： K Akshitha Roopashree R Ashwini Kodipalli Trupthi Rao Dept of AI and DS Global Academy of Technology Banglore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

the persistence of lung cancer on the global health scene necessitates the need for early and accurate techniques that may improve patient survival. Recent breakthroughs in Artificial Neural Networks (ANNs) have shown potential in medical image analysis mainly used in detecting and classifying lung cancer from imaging data including clinical datasets. the current study investigates the utilization of ANN models in detecting lung cancers using datasets classified into malignant and benign. the approaches involve elaborate data preprocessing, optimizing ANN architecture with different activation functions, and incorporating dropout mechanism to reduce overfitting. Furthermore, findings indicate that optimized ANN models give high accuracy rates of up to 95% comparable to traditional machine learning techniques thus establishing their efficiency in noninvasive lung cancer detection.

关键词： Technological innovation Accuracy Lung cancer Artificial neural networks Transforms Predictive models pattern recognition Reliability Predictive analytics Random forests

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Enhancing Few-Shot learning with Integrated data and GAN Model Approaches

Enhancing Few-Shot Learning with Integrated Data and GAN Mod...

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Digital Society and Intelligent Systems (DSInS), international conference on

作者： Yinqiu Feng Aoran Shen Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du Columbia University New York USA University of Michigan Ann Arbor USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

this paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, the paper addresses the noise and bias issues introduced by data augmentation methods, contrasting them with model-based approaches, such as fine-tuning and metric learning, which rely heavily on related datasets. By combining Markov Chain Monte Carlo (MCMC) sampling and discriminative model ensemble strategies within a GAN framework, the proposed model adjusts generative and discriminative distributions to simulate a broader range of relevant data. Furthermore, it employs MHLoss and a reparameterized GAN ensemble to enhance stability and accelerate convergence, ultimately leading to improved classification performance on small-sample images and structured datasets. Results confirm that the MhERGAN algorithm developed in this research is highly effective for few-shot learning, offering a practical solution that bridges data scarcity with high-performing model adaptability and generalization.

关键词： Training Adaptation models Target recognition Generative adversarial networks data augmentation data models Drug discovery Artificial intelligence Few shot learning Convergence

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machine learning-Based Scene Classification Using thepade’s SBTC, LBP, and GLCM 4th

Machine Learning-Based Scene Classification Using Thepade’s...

引用

4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： thepade, Sudeep D. Idhate, Mrunal E. Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India

ISBN: (纸本)9789811950360

Photographs have become a necessary part of human life. Images are being captured all across the world. One of the significant issues in scene classification is that the photographs are taken in a variety of locations. there are many diverse scenes in these locations, such as an airport, a restaurant, a cafeteria, and a forest. Indoor scene classification and outdoor scene classification are the most common types of scene categorization. Scene classification is challenging, which poses issues in research and image processing. Image classification becomes challenging because of the numerous distinct items present in various sceneries, causing a machine to become confused about the scene’s categories. the current challenge with scene classification results in low-classification accuracy. the object comes in various shapes and sizes and can be found in various locations, which is one of the reasons for the scene classification’s poor accuracy. We will perform feature extraction for pattern recognition and multiple machine learning models for image categorization using the intel image classification dataset. thepade’s SBTC n-ary feature extraction approach for RGB and LUV color plane models and fusion LBP and GLCM is presented in this study. When the three individual feature extraction methods are compared with the fusion of thepade’s SBTC and GLCM with RGB color plane, the fusion of thepade’s SBTC and GLCM with RGB color plane yielded greater accuracy. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Research on Employee Turnover Prediction Based on machine learning Algorithms 4

Research on Employee Turnover Prediction Based on Machine Le...

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4th international conference on Artificial Intelligence and Big data, ICAIBD 2021

作者： Yuan, Jia Chengdu Institute of Public Administration Chengdu China

ISBN: (纸本)9780738131702

Great changes are taking place in enterprise management in the era of big data. As an important part of the company, human resource management is also affected by big data. Starting from human resources combined with data mining, this paper takes the working data of enterprise employees as the main data, through the analysis of data, to understand what factors affect the resignation of employees, the main reasons, and predicts which excellent employees will leave. through the current popular algorithms (SVM, random forest, neural network, logistic regression, decision tree) to predict the indicators, this research provides a scientific basis for the precise management of enterprises. the contents of this study include: analyzing the factors leading to employee turnover and mining the influence degree of relevant factors;building model through algorithm, and finding the best parameters of the model to predict which employees may leave;selecting the best model and parameters through model comparison. through the data mining of the departed employees, we can find out the factors affecting the turnover of employees, and make a conclusion based on the analysis results and the actual situation of the enterprise. © 2021 IEEE.

关键词： Decision trees

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Spatiotemporal Visual Tracker Based on Deformable Attention

Spatiotemporal Visual Tracker Based on Deformable Attention

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Zhuo Wu Askar Hamdulla Yucheng Huang School of Computer Science and Technology(School of Cyberspace Security) Xinjiang University Urumqi China School of Intelligence Science and Technology(School of Future Technology) Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%~5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods.

关键词： Visualization Target tracking Semantics Transformers Feature extraction Spatiotemporal phenomena pattern recognition Convolutional neural networks Information exchange Resilience

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the 4th Artificial Intelligence of things (AIoT) Workshop 21

The 4th Artificial Intelligence of Things (AIoT) Workshop

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Zhang, Jian Tang, Jian Chen, Yiran Liu, Jie Ye, Jieping Wolf, Marilyn Narayanan, Vijaykrishnan Srivastava, Mani Jordan, Michael, I Bahl, Victor Microsoft Redmond WA 98052 USA Midea Grp Shenzhen Peoples R China Duke Univ Durham NC 27706 USA Harbin Inst Tech Shenzhen Peoples R China Beike Beijing Peoples R China Univ Nebraska Lincoln NE USA Penn State Univ University Pk PA 16802 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781450383325

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. this enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of things) that supports applications such as smart city/agriculture/manufacturing/health care and self-driving scenarios. the AIoT Workshop is a forum for researchers, scientists, engineers, and practitioners to share and learn AI powered IoT solutions. the AIoT is a multi-disciplinary area, which include but not limited to IoT, AI/ML, embedded systems, and networking. the 4th AIoT workshop will be hosted virtually in conjunction with the 27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD 2021). the workshop program consists of keynotes, invited talks, accepted technical paper presentations, as well as an indoor location competition panel.

关键词： internet of things machine learning artificial intelligence cyber physical systems sensor 5g network power

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Classifying Eye-Tracking data Using Saliency Maps 25

Classifying Eye-Tracking Data Using Saliency Maps

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25th international conference on pattern recognition (ICPR)

作者： Rahman, Shafin Rahman, Sejuti Shahid, Omar Abdullah, Md Tahmeed Sourov, Jubair Ahmed North South Univ Elect & Comp Engn Dhaka Bangladesh Univ Dhaka Robot & Mechatron Engn Dhaka Bangladesh

ISBN: (纸本)9781728188089

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-Iixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2], [4], 5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

关键词： Eye-tracking Visual Saliency Autism Spectrum Disorder Toddler Age Prediction Visual Perceptual Task

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A method of Named Entity recognition in Classical Chinese based on Bert-Ancient-Chinese

A method of Named Entity Recognition in Classical Chinese ba...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Ping Feng Jingyu Kang Biao Huang Shulai Wang Jialun Li Jilin University Changchun Jilin China Changchun University Changchun Jilin China Jilin Provincial Key Laboratory of Human Health State Identification and Function Enhancement Changchun Jilin

At present, in the field of Natural Language Processing (NLP), the accuracy of pre-trained models for downstream tasks such as Named Entity recognition (NER) is significantly improved. Named Entity recognition is a key technology in Knowledge Graphs (KG), but there are relatively few studies on this technology in the field of Classical Chinese. this paper proposes a Classical Chinese Named Entity recognition model based on bert-ancient-chinese, which is based on bert-ancient-chinese+Recurrent Long Short-Term Memory+Conditional Random Field (BAC+RLSTM+CRF) for Named Entity recognition and tested on the C-CLUE dataset. Compared with the baseline of the dataset, the F1 value is improved by about 22.11%.

关键词： Training Semantics machine learning Knowledge graphs Big data Benchmark testing Natural language processing

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Prediction of Unsteady Indoor-Temperature with Few pattern data learning and Prediction Model Selection Based on Feature Contribution 4

Prediction of Unsteady Indoor-Temperature with Few Pattern D...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Maejima, Sotaro Tsunoda, Keisuke Kodama, Midori Arai, Naoki Obana, Kazuaki NTT Smart Data Science Center Nippon Telegraph and Telephone Corporation Tokyo Japan

ISBN: (纸本)9781665434102

the aim of this paper is to predict indoor-temperature by machine learning under three main constraints: 1) indoor-temperature is unsteady due to people flow, 2) only data with few control patterns of air-conditioning can be used for training, and 3) indoor-temperature is to be accurately and plausibly predicted under unknown air-conditioning control patterns not included in training data. Previous studies tried to predict indoor-temperature in buildings without people but with air-conditioning data for various control patterns. However, these constraints make predictions of indoor-temperature irregular because of unsteady indoor-temperature and inadequate data patterns. To solve the problems, we propose a model selection method based on prediction accuracy and feature contribution. the method can select the prediction model appropriate for the observed instability and can augment data patterns We demonstrate the effectiveness of our proposal using measured sensor data. © 2021 IEEE

关键词： Air conditioning

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Speech Emotion recognition Using 2D-CNN with data Augmentation 7

Speech Emotion Recognition Using 2D-CNN with Data Augmentati...

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7th international conference on Software Engineering and Computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Mujaddidurrahman, Auliya Ernawan, Ferda Wibowo, Adi Sarwoko, Eko Adi Sugiharto, Aris Wahyudi, Muhammad Didik Rohmad Diponegoro University Department of Informatics Semarang Indonesia Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Malaysia Universitas Islam Negeri Sunan Kalijaga Yogyakarta Indonesia

ISBN: (纸本)9781665414074

Speech emotion recognition becomes a challenging issue, especially interaction between human-machine. Each person expressed their emotions in various ways and the features of speech are still unclear to distinguish between existing emotions. the results of speech were caused by mental and psychological states where it is directly influenced by emotions. this research proposed speech emotion recognition based on the 2D-CNN model with Log-Mel spectrogram as the input. the proposed method of the 2D-CNN model with Log-Mel spectrogram was able to capture the significant speech signals. the experiments used the EMODB database to evaluate the proposed model with augmented data. the proposed model was able to produce higher accuracy of speech emotion than the existing models, which also used deep learning methods for recognizing emotions. the experimental results showed that the proposed model achieved an accuracy of 0.88. © 2021 IEEE.

关键词： Neural network models

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