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检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 564 条记录，以下是91-100 订阅

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A Proposal of Deep learning Model for Classifying User Interests on Social Networks 2020

A Proposal of Deep Learning Model for Classifying User Inter...

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4th international conference on machine learning and soft computing (icmlsc)

作者： Xuan Truong Dinh Hai Van Pham CMC Inst Sci & Technol Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9781450376310

In the recent years, there are huge data extracted from social networks in both static and real-time analysis, such as Facebook, Twitter, LinkedIn, and Instagram. Recently, most researchers have investigated in classifying textual contents without user interests/behaviors from huge data of social networks. this paper has presented a novel approach using a Convolution Neural Network with its new contribution of user perceptions from the social network data to classifying the user interests. Experimental results show that the proposed model performs better than the conventional algorithms in terms of classifying user interests. Additionally, the proposed model enhances a quality of classification for user interests tracking real-time in social networks.

关键词： User Interest Deep learning Convolution Neural Network Online Social Network

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An Ingenious Method to Detect COVID in X-Ray Images Using machine learning Techniques 4

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Kumari, Palak Rani, Neha Suresh Kumar, N. Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9781665474368

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. the World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. the COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. the global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. this paper proposes a deep neural network modification based on the Xception model. the model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. the layer addition is intended to avoid overfitting the proposed model. the proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. the architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.

关键词： COVID-19

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Plant Disease Classification Using Deep learning Methods 2020

Plant Disease Classification Using Deep Learning Methods

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4th international conference on machine learning and soft computing (icmlsc)

作者： Wan, Hu Lu, Zheng Qi, Wang Chen, Yuanyuan SiChuan Univ Dept Comp Sci Chengdu Peoples R China

ISBN: (纸本)9781450376310

Nowadays, the development of agriculture is of great practical significance to all regions and countries. According to statistics, as a large agricultural country, China's annual food loss caused by insufficient pest prevention and control capacity exceeds 30% of the total loss, and its direct economic loss amounts to billions of CNY. the worldwide data is even more huge, so the detection of pests and diseases is particularly important. In the traditional agricultural field, diseases control mainly relies on the accumulated experience of farmers themselves, which is not always stable and requires a long time to form. In this paper we propose a suitable and accurate method for agricultural diseases detection, and finally achieve about 87% accuracy on a relatively large dataset.

关键词： Agriculture Plant Disease Deep learning Convolution Neural Networks

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A Study of Optimizations in Rainfall Prediction using machine learning Models

A Study of Optimizations in Rainfall Prediction using Machin...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Chaitanya Kumar Sinha Udit Sharma M. Maragatharajan J. Manikandan School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Rainfall prediction is essential for many industries, such as agriculture, water resource management, and disaster relief. Recently, there has been a lot of interest in machine learning techniques to increase the accuracy of rainfall forecasts. To improve rainfall prediction models' accuracy, we provide a hybrid strategy in this study that combines conventional algorithms with cutting-edge optimization techniques. We utilize the XG Boost, Random Forest, and Linear Regression algorithms as baseline models. these are then combined with the Crow Search Algorithm (CSA) optimization technique. Crow collective behavior inspired the nature-inspired metaheuristic algorithm, CSA, which has demonstrated promising results in solving complicated problems. this study intends to improve the baseline models' performance by dynamically adjusting their parameters through the use of CSA. the proposed approach incorporates CSA with the chosen machine learning algorithms, which adaptively modify parameters to increase prediction accuracy. We validate the suggested approach with historical rainfall data and conduct extensive experimentation to show its efficacy.

关键词： Adaptation models Rain Accuracy machine learning algorithms Heuristic algorithms Humidity Predictive models Prediction algorithms Data models Forecasting

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Transformative Potential of Consumer Insights into Sustainable Business Growth: machine learning Integrated with Marketing Analytics

Transformative Potential of Consumer Insights into Sustainab...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Ayush Bist Gurpreet Singh Shilpee Srivastava Department of Mathematics Chandigarh University Chandigarh India Department of CSE Chandigarh University Chandigarh India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

machine learning and marketing analytics have emerged as a well-known tool for transforming consumer insights into substantial business growth. through the consumption of advanced procedures and data analytics methodologies, enterprises gain the ability to extract invaluable insights from extensive consumer data sets, leading to refined marketing strategies and enriched customer experiences. this research study analyzes the synergy between machine learning and marketing analytics, emphasizing their profound impact on consumer insights and subsequent business expansion. It explains a range of machine learning algorithms and data analytics tools pertinent to marketing endeavors, including predictive modeling, customer segmentation, and recommendation systems. providing businesses with robust tools for decision-making and strategy formulation. Additionally, this research study explores the real-world examples showcasing the transformative potential of machine learning and marketing analytics in optimizing marketing campaigns, personalizing customer interactions, and enabling sustainable growth. By integrating machine learning and marketing analytics, organizations can capitalize on unparalleled opportunities to gain a competitive advantage in today’s data-driven scenario, translating consumer insights into actionable strategies with unprecedented accuracy and efficacy.

关键词： Analytical models Data analysis machine learning algorithms Decision making machine learning Organizations Predictive models Market research Security Recommender systems

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Android Malware Detection and Classification using Stacked machine learning 4

Android Malware Detection and Classification using Stacked M...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Nawshin, Faria Gad, Radwa Unal, Devrim Suganthan, Ponnuthurai Nagaratnam KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Department of Computer Science & Engineering College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9781839539954

the widespread use of Android smartphones in daily life can be attributed to the extensive prevalence stemming from the Android OS and the availability of open-source applications. People have become accustomed to performing various tasks, such as online banking transactions, scheduling appointments, attending virtual meetings and classes, and online shopping, on their mobile devices instead of computers. However, the significant use of mobile phones also presents opportunities for attackers to access sensitive and confidential information without the user’s knowledge. Intruders often use malicious applications found on the Google Play Store and other third-party app markets. the increased volume of Android malware in recent years has severely threatened human privacy and security. In this paper, we present a novel solution that utilizes a stacking ensemble machine learning approach to detect whether an application is malware or benign, which achieved 98% accuracy and F1-Score, and to identify the malware category with 96% accuracy and F1-Score. We conducted the experiment on the CCCS-CIC-AndMal-2020 dataset, which comprises 27,125 samples. Static features such as permissions, meta-data, and intents were analyzed, and the performance of standard machine learning algorithms was compared to our proposed model. Our results demonstrate that the stacked ensemble model shows promising results for malware detection (binary classification) and malware type classification (multi-class classification). © 2024 the Author(s).

关键词： Android malware

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Behavior Identification in ATM using Different Methods: A Review

Behavior Identification in ATM using Different Methods: A Re...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Utsav Kalariya Chetan Soni Vikas Dubey Department of Information Technology Parul Institute of Engineering & Technology FET Parul University Vadodara Gujarat India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the rapid evolution of Automated Teller machines (ATMs) has intensified the need for effective behavior identification methods to combat fraudulent activities and enhance security. this review provides a comprehensive analysis of current methodologies used in ATM behavior identification, including machine learning, deep learning, and video surveillance techniques. Despite the advancements, there remains a critical research gap in addressing the integration and effectiveness of these methods in diverse real-world scenarios. this paper evaluates and compares the efficacy of approaches such as YOLOv3-based detection, Convolutional LSTM networks, and heuristic algorithms. It identifies existing gaps, such as the lack of comprehensive datasets and the need for real-time detection capabilities. By synthesizing recent advancements and identifying these gaps, the paper aims to offer valuable insights into improving behavior identification strategies and to propose future research directions for enhancing ATM security systems.

关键词： Deep learning machine learning algorithms Reviews Automated teller machine Prevention and mitigation Heuristic algorithms Video surveillance Real-time systems Security Long short term memory

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An ensemble multi-objective particle swarm optimization approach for exchange rates forecasting problem 2020

An ensemble multi-objective particle swarm optimization appr...

引用

4th international conference on machine learning and soft computing (icmlsc)

作者： thi thu Huong Dinh Van Truong Vu Lam thu Bui Sai Gon Univ Ho Chi Minh Vietnam Le Quy Don Tech Univ Hanoi Vietnam

ISBN: (纸本)9781450376310

In this paper, the authors propose an ensemble multi-objective particle swarm optimisation approach (named EMPSO) for forecasting the currency exchange rate chain. the proposed algorithm consists of two main phases. the first phase uses a multi-objective particle swarm optimisation algorithm to find a set of the best optimal particles (named leaders). the second phase then uses these leaders to jointly calculate the final results by using the soft voting ensemble method. the two objective functions used here are predictive error and particle diversity. the empirical data used in this study are six different sets of currency exchange rates. through comparison results with other evolutionary algorithms and other multi-objective PSO algorithms, the proposed algorithm shows that it can achieve better as well as more stability results on experimental data sets.

关键词： Time series forecasting PSO multi-objective PSO ensemble learning.

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Survey of Deep learning Techniques for Malware Detection: Insights, Challenges, and Future Directions

Survey of Deep Learning Techniques for Malware Detection: In...

引用

soft computing for Security Applications (ICSCSA), international conference on

作者： Kshatriya Vinaya Sree Bai M. thirumaran Department of Computer Science and Engineering Puducherry Technological University (PTU) Puducherry Tamilnadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Android and Windows become the main platforms for mobile and personal computing, the threat from advanced malware targeting these systems is increasing. Traditional machine learning methods are struggling to keep up with these sophisticated threats, so deep learning techniques, which have shown better results, are needed. this paper reviews various deep learning methods for detecting and classifying malware across different environments, like IoT and web platforms. We examined 40 research articles from major digital libraries, focusing on different deep learning models like CNNs, LSTMs, DBNs, and *** analysis highlights the strengths and weaknesses of these models, such as high false positive rates and difficulties with detecting hidden malware. In order to decrease false positives and increase detection accuracy, we provide a novel framework that integrates many deep learning methods. this survey offers a comprehensive guide for new researchers and practical insights for improving malware detection with deep learning.

关键词： Deep learning Surveys Analytical models Technological innovation Reviews Autoencoders Collaboration Transformers Malware Long short term memory

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A Comprehensive Analysis of AI Methods for Cervical Cancer Detection

A Comprehensive Analysis of AI Methods for Cervical Cancer D...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Remya R Kumudha Raimond Department of CSE KITS Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cervical cancer is a significant health concern, being the fourth most common and deadliest cancer among women. Early detection is crucial for successful treatment, with the Pap smear test being the most commonly used method. However, manual screening can be time-consuming and prone to human error, leading to high false positive rates. In order to improve screening accuracy, researchers have developed many machine learning as well as deep learning approaches for the automatic segmentation and classification of cell pictures. In this paper, we conducted a comprehensive study of the different computer-aided diagnostic methods used for analysing cervical cytology images. the research findings offer valuable insights into the state-of-the-art in this field, highlighting the potential of these advanced technologies to enhance cervical cancer screening and save lives.

关键词： Deep learning Image quality Image segmentation Noise Manuals Security Cervix Cervical cancer Tuning Overfitting

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