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A machine learning-Based Predictive Model for the Management of Incidents in Small and Medium-Sized Enterprises in Peru 24

A Machine Learning-Based Predictive Model for the Management...

引用

3rd Asia conference on Algorithms, Computing and machine learning, CACML 2024

作者： Cribillero, Luis F. Quispe, Jeyson I. Castañeda, Pedro Faculty of Information Systems Engineering Universidad Peruana de Ciencias Aplicadas Peru

ISBN: (纸本)9798400716416

In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics provided by the scikit-learn library. Additionally, the entropy loss approaches a value of 0, suggesting a precise ability of the model to predict real values. Additionally, an average accuracy level of 93.74% was achieved. © 2024 ACM.

关键词： Adversarial machine learning

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Enhanced data Mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

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Diverse Image Processing Techniques for machine learning Methods 3

Diverse Image Processing Techniques for Machine Learning Met...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Bhagya, A. Perumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350361186

machine learning is a trending topic in the area of computer vision, which makes the machine able to learn about it without being expressly programmed using various algorithms. When a model is designed to predict future-based on historical data using machine learning algorithms, data is vital because high quality data is needed to run the model efficiently and to predict highly accurate results. machine learning algorithms receive data in many forms, like audio (speech recognition), images (feature recognition), numeric data (like height and weight) etc. When it comes to computer vision, the machine learning algorithms must be able to understand the image data, and before the images are processed to the algorithms it should be pre-processed (cleaned and set to the desired format) using various image pre-processing techniques. This research paper presents various image processing techniques, literature reviews on various image processing techniques and image PSNR value after applying median filter, grayscale filter and box filters. © 2024 IEEE.

关键词： Median filters

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Prediction Of Autism Spectrum Disorder Using machine learning 3

Prediction Of Autism Spectrum Disorder Using Machine Learnin...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Arunprasath, T. Niranjana, M. Sharumathi, M. Buvanesh Pandian, V. Rajasekaran, M Pallikonda Ramaraj, Kottaimalai Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Eee Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Ece Krishnankoil621 216 India

ISBN: (纸本)9798350361186

This is to investigate the predictive potential of electroencephalogram (EEG) signals extracted from children diagnosed with autism spectrum disorder (ASD). The objective is to utilize machine learning techniques, specifically logistic regression, to discern patterns within EEG data that could aid in the early prediction of ASD. EEG signals collected from autism-affected children are analyzed and processed to extract relevant features. Subsequently, these features are used as inputs to train a logistic regression model. Thestudy evaluates the model's performance in distinguishing between ASD and typically developing individuals, aiming to provide a reliable predictive toolfor identifying ASD based on EEG signals. Results suggest promising prospects for leveraging EEG data and machine learning methods in the early detection and potential prediction of autism spectrum disorder in children. © 2024 IEEE.

关键词： Electroencephalography

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RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL engineering STRUCTURES USING machine VISION TECHNOLOGY 43

RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGIN...

引用

ASME 43rd international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Yu, Qifeng Han, Yudong Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

ISBN: (纸本)9780791887806

The harsh marine atmospheric conditions, including high temperatures, humidity, and salt spray, prevalent in the coastal areas of Hainan, pose a significant challenge to the durability of ground facilities and equipment, often resulting in corrosion and functional degradation. Hence, accurate monitoring of corrosion is paramount for maintaining coastal engineering structures. This study leveraged data from the Chinese National Center for Materials Corrosion and Protection Science to extract corrosion morphology features using image analysis techniques. Subsequently, a corrosion identification model was developed using machine learning algorithms. The model's accuracy was validated through various evaluation metrics. The research outcomes have practical applications in the maintenance and management of coastal engineering structures by providing automatic corrosion morphology recognition. This enables maintenance personnel to promptly undertake repair measures, thereby reducing maintenance costs and enhancing structural sustainability.

关键词： Corrosion morphology machine learning techniques image processing YOLO v5 automatic recognition

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data Augmentation Method for Classification of Lymph Node Metastasis using Diffusion Model 3

Data Augmentation Method for Classification of Lymph Node Me...

引用

3rd international conference on Image Processing and Robotics, ICIPRoB 2024

作者： Suzuki, Kosuke Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Ouchi, Akira Shimizu, Yasuhiro Nagoya Institute of Technology Department of Computer Science Aichi Japan Chubu University Department of Computer Science Aichi Japan Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati India Aichi Cancer Center Hospital Aichi Japan

ISBN: (纸本)9798350374766

Automatic judgment of the presence or absence of cancer metastasis to lymph nodes is an important topic in cancer staging to reduce the burden of medical doctors to check a large number of CT images. Therefore, the machine learning approach becomes helpful and desired. However, using CT images for machine learning requires annotation by a special radiologist, and it is difficult to prepare a large dataset. In recent years, many approaches to the generation of images by machine learning have been proposed and applied to medical images. In this paper, we propose a method to generate images around a lymph node in an arbitrary metastatic state using a diffusion model, which is a generative model that is gaining attention on behalf of GAN. Experiments were also conducted to learn the classification of cancer metastasis using the generated images for data augmentation, showing that the generated images are effective in improving classification performance. © 2024 IEEE.

关键词： Medical imaging

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Research on English Online Classroom learning System Based on Computer Intelligent Assistive Technology 3

Research on English Online Classroom Learning System Based o...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Guangjie, Shen Qi, Wang Tianli, Zhang Qingling, Wang Weifang Engineering Vocational College Weifang China

ISBN: (纸本)9798350380989

The English learning system structure designed in this article is mainly based on the B/S three-level architecture. A relational database uses SQLite as the data layer. The system uses the Bootstrap system to automatically recognize English words, and uses custom tags to enable teachers to quickly review test papers and provide feedback. An automatic image recognition technology that combines similarity comparison based on field context and CNN+BLSTM is proposed. This paper analyzes the user registration model and query process, uses multiple information technologies to ensure the stability of online teaching and further expand teaching methods. Experiments have proven that the proposed English intelligent dictionary recommendation system has the same characteristics as the age similarity weight, and both show an increasing trend as the parameters increase. The system can clearly display the records of words that the learning object has learned after logging into the system, including the number of times the word has been used, achieving intelligent effects. © 2024 IEEE.

关键词： Computer English teaching image recognition intelligence intelligent teaching teaching system

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The Application of machine learning Techniques to the Diagnosis of Breast Cancer 3

The Application of Machine Learning Techniques to the Diagno...

引用

3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Prasad, Pratyush Manipal University Data Science and Engineering Jaipur India

ISBN: (纸本)9798350320749

Due to its high mortality rate and limited survival duration, early identification of breast cancer is vital. The primary application of machine learning models has been the prediction and early diagnosis of breast cancer. This article presents machine learning models and algorithms for automated breast cancer diagnosis using an exploratory approach. The technique makes use of eight distinct machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, XG-Boost, and Deep learning Model. Their precision is used to evaluate the performance of algorithms and models. Additionally, probabilistic predictions have been made using activation functions like Relu. In terms of test accuracy on the Kaggle data set, Deep learning Model 2 and the Support Vector Classifier fared the best. © 2023 IEEE.

关键词： Decision trees

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Development of A Multimodal Deep Feature Fusion with Ensemble learning Architecture for Real-Time Gas Leak Detection 3

Development of A Multimodal Deep Feature Fusion with Ensembl...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Zhang, Eddie Zhang, Evan Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

Gas leak detection is extremely critical, but poses many significant challenges due to the high cost and large amount of human labor and expertise necessary to complete an inspection. Our previously reported soft robot SPIRo system is designed to address these challenges. This paper proposes a multimodal deep feature fusion system with the deep forest ensemble learning model, which fuses data from gas sensors and thermal images to improve SPIRo's accuracy and redundancy. The model consists of the convolutional layers of three CNNs: the AlexNet, ResNet-50, and MobileNet, and the extracted deep features from them along with the measured gas sensor data are fused into a Deep Forest Classification (DFC) module to make the final decision on whether a gas leak is present or not. The combination of a single CNN and gas sensor data fused with a DFC is also evaluated. Two sets of data have been tested with this system. The results indicate that for the training database, the ensemble model with three CNNs achieved a detection accuracy of 98.5%, compared to 95.0% for the ensemble model with single CNN and 93.1% achieved by each individual model. For the testing database collected by SPIRo, the ensemble model with three CNNs achieved an accuracy of 88.1%, compared to 82.4% for the single CNN models with ensemble and around 73.6% for the individual models. The results from experimental data collected by SPIRo furthers the architecture's effectiveness. Not only is the proposed method more accurate, but it is also more reliable due to increased redundancy in analyzing both thermal images and gas sensor data. In addition, it is also memory efficient, making it suitable for real-time detection applications.

关键词： Multimodal Fusion Deep Feature Fusion Ensemble learning Deep Forest Thermal Imaging Gas Leak Detection

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Region-Wise Weather data-Based Crop Recommendation Using machine learning Algorithms 3

Region-Wise Weather Data-Based Crop Recommendation Using Mac...

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3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Ghilan, Akram Afou, Youssef El Akkad, Nabil El Fez28810 Morocco

ISBN: (纸本)9798331522056

Annually, numerous agricultural crops suffer significant losses due to insufficient technical knowledge and unpredictable weather patterns, including fluctuations in precipitation, temperature, and other environmental conditions crucial for crop yield and profitability. Selecting appropriate crops to enhance yield is vital for improving real-life farming scenarios. In this context, crop recommendation systems leveraging IoT data in smart agriculture have emerged as invaluable tools in modern farming practices. These systems increasingly utilize machine learning techniques to determine the most adequate crops based on continuously measured soil, environmental, and weather parameters collected by IoT sensors. This manuscript investigates the integration of cutting-edge machine learning algorithms for crops selection, employing an open dataset of 2200 data points covering 22 crops. We compare the performance of Gaussian Naive Bayes, Random Forest, Extra Trees Classifier, Decision Trees, Logistic Regression, k-Nearest Neighbors and Support Vector machines in determining suitable crops based on soil and weather parameters. Our results demonstrate notable improvements in the accuracy of crop selection, with Gaussian Naive Bayes achieving an accuracy of 99.82%, outperforming other algorithms in both accuracy and computational efficiency. This approach offers a viable solution to empower farmers with tools for improved farm productivity and sustainable agriculture. Finally, we discuss the potential impact of these systems on sustainable farming practices. © 2024 IEEE.

关键词： Smart agriculture

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