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Detection of Lungs Cancer through Computed Tomographic Images Using Deep learning 4

Detection of Lungs Cancer Through Computed Tomographic Image...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Abid, Madiha Akbar, Shahzad Abid, Sabeen Hassan, Syed Ale Gull, Sahar Riphah International University Department of Computing Faisalabad Pakistan

ISBN: (纸本)9781665463775

Lung cancer has become a particularly lethal disease in the last decade. Lung cancer is the second most common cause of death for women and the primary cause of death for men. therefore, early detection of lung knobs is one of the most effective ways to treat lung infections. Similarly, computer-Aided diagnosis (CAD) of lung knobs has gotten a huge interest over the last decade. As a result of the broad variety of lung knobs and the complications of the entire environment, developing a robust knob detection approach is extremely difficult. A convolutional neural network (CNN) based framework is proposed to detect tumors that are identified as risky or benign in lung disease screening using CT images. Two publicly available datasets LUNA-16 and LIDC are employed to detect lung cancer. the dataset is augmented to maximize the volume of images in it. Also, preprocessing is done on CT images for better noise removal. Additionally, segmentation is performed to specify the infected area. three pre-Trained architectures, DenseNet, AlexNet, and VGG-16, are utilized to classify the cancerous and normal images. the DenseNet classifier achieved 98% classification accuracy, 98.93% sensitivity, and 99% specificity, which exhibits outstanding performance than other classifiers. the efficient results of the proposed framework show better performance than existing state-of-Art studies. © 2023 IEEE.

关键词： Convolutional neural networks

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Optimizing Access Control Systems with Automatic Plate Number Recognition: A YOLOv9 Deep learning Approach 4

Optimizing Access Control Systems with Automatic Plate Numbe...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Asaju, Christine Bukola Owolawi, Pius Adewale Du, Chunling Van Wyk, Etienne Computer Systems Engineering Tshwane University of technology Pretoria South Africa

ISBN: (纸本)9798350353266

Controlling access to a restricted area requires the use of fast and robust technology. this study proposes a model for access control that uses automatic plate number recognition (APNR) technology based on deep learning architecture, You Only Look Once, version 9 (YOLOv9). Conventional access control mechanisms often rely on manual verification processes, leading to inefficiencies and security vulnerabilities. In contrast, our proposed system integrates real-time APNR with YOLOv9, facilitating the rapid and precise identification of vehicles based on their plate numbers. YOLOv9's robust architecture enhances detection accuracy and computational efficiency, making it well-suited for deployment across diverse access control scenarios. the proposed method makes use of the YOLOv9 architecture's improved ability to detect and recognize plate numbers from vehicle images in real-time, to control vehicle access to a restricted area. the model was trained and validated using an open-source vehicle plate number dataset, reporting a precision of 73 % and a Mean Average Precision (mAP) of 71 % at a threshold of 0.5. Experimental tests with live samples demonstrated how well and consistently the proposed method performs in different environmental situations, including lighting, weather, and vehi-cle orientations. the findings demonstrated the YOLOv9-based system's effective application in real-world circumstances, such as boom gate access, gate access, and so on. © 2024 IEEE.

关键词： Deep learning

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Enhancing Natural Language Processing with machine learning for Conversational AI 4

Enhancing Natural Language Processing with Machine Learning ...

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

作者： Bataineh, Abdallah Q. Abu-AlSondos, Ibrahim A. Almazaydeh, Laiali El Mokdad, Soha Salem Allahham, Mahmoud Digital Marketing Department Al-Zaytoonah University of Jordan Amman Jordan IT Management Department American University in the Emirates Dubai United Arab Emirates College of Computer IT American University in the Emirates Dubai United Arab Emirates Sharjah Women’s College Higher Colleges of Technology Sharjah United Arab Emirates Business Administration Department Middle East University Amman Jordan

ISBN: (纸本)9781839539954

this research focuses on improving NLP abilities using machine learning to build intelligent conversation agents. the information fetched through the literature review and the approach to the research methodology includes collecting, processing, and testing the models with big NLP datasets. the research's goal is to intensely improve the quality of conversational AI by fine-tuning state-of-the-art NLP models for a conversational context. this research was conducted in Jordan, where they exploited the region's unique features and dialects for enhanced knowledge of the natural language by AI, thereby improving the possible uses of conversational AI globally. the expected outcomes are twofold: enhanced conversational capabilities for conversational AI and a significant research contribution to the knowledge base, especially in customer service and virtual assistance. the impact of the research is far-reaching as improvements in conversational AI are expected to ease cross-sector operations, improve user experience, and lay a foundation for future developments in language-based AI interaction. the research stands at the cutting edge of technological development in Jordan and may serve as a model for other regional Conversational AI innovations. the use of machine learning combined with NLP is not only moving forward the conversation abilities of artificial intelligence systems, but it is also preparing the ground for next-generation advancements to change the nature of human-computer interaction. the realization of the findings can be instrumental in opening up a whole new era of AI whereby it talks, comprehends, and even satisfies the needs of humans through unparalleled eloquence and accuracy. © the Institution of Engineering & Technology 2023.

关键词： Chatbots

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Exploring the thematic Clusters of Artificial Intelligence applications in Supply Chains Using Topic Modelling and Text Mining: A machine learning Insight 4

Exploring the Thematic Clusters of Artificial Intelligence A...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Olasiuk, Hanna Kumar, Sanjeev Singh, Sudhanshu Nagpal, Gaurav Ganushchak, Tetiana Op Jindal Global University Jindal Global Business School Haryana Sonipat India Galgotias University School Of Business Uttar Pradesh Gautam Buddh Nagar India Birla Institute Of Technology And Science Pilani Department Of Management Pilani Campus Rajasthan India State University Of Trade And Economics Economic And Finance Enterprise Department 19 Kyoto st. Kyiv Ukraine

ISBN: (纸本)9798350384628

Artificial Intelligence (AI) applications are vital for optimising supply chain operations, enhancing efficiency in logistics, inventory management, and demand forecasting. AI's real-time data analysis capabilities empower decision-making and risk management. Despite significant growth in AI supply chain research over the past two decades, a notable gap exists in comprehensive reviews that synthesise key themes. To address this, this study combines bibliometric analysis and structural topic modelling, examining a dataset of 773 articles from Elsevier's Scopus database spanning 2002 to 2022. this study focuses on AI applications in supply chains, emphasising theme identification, publication trends, and prominent journals. Notably, the "international Journal of Production Research"emerges as the top source, with 46 articles, constituting 6.0% of the total articles. the research uncovers four thematic clusters: AI-driven cost optimisation, AI-powered decision-making, mitigating supply chain risks, and AI integration in technological advancements. Future studies in artificial intelligence applied to supply chain management should focus on employing sophisticated algorithms. At the same time, it is essential to consider issues related to privacy and security. Additionally, researchers should tackle environmental obstacles and ensure compliance with regulatory standards. these findings enhance the understanding of AI applications in supply chains, providing insight into key themes and essential keywords. these findings are highly pertinent to various audiences, including researchers, financial institutions, and industry professionals. © 2023 IEEE.

关键词： machine learning

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Efficient Estimation of Cow's Location Using machine learning Based on Sensor Data 4th

Efficient Estimation of Cow's Location Using Machine Learnin...

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4th European-Alliance-for-Innovation (EAI) international conference on Artificial Intelligence for Communications and Networks (AICON)

作者： Sawada, Tomohide Uchino, Tom Martono, Niken P. Ohwada, Hayato Tokyo Univ Sci Fac Sci & Technol Dept Ind Adm Tokyo Japan

ISBN: (纸本)9783031291258;9783031291265

Indoor localization of dairy cows is important for determining cow behavior and enabling an effective farm management. In this study, a low-cost localization system was constructed by attaching accelerometers to dairy cows kept indoors in a barn in order to obtain radio wave strength. Using link quality indicator (LQI) data, we employed four machine learning models to predict the position of the cow: LightGBM, logistic regression, support vector machine (SVM), and neural network. the prediction performance and computational cost of the models were compared and evaluated. In the monitoring and building of the prediction models for cow's location, we considered various sizes of location (barn) compartments and evaluated the performance of each prediction model using with different compartments. the experimental results showed that LightGBM and neural networks have an accuracy of 46.6% at 9m horizontal and 12m vertical and an accuracy of 90% at 45m horizontal and 15m vertical. In terms of the computational score, we may consider whether to use neural network or LightGBM depending on the amount of data to be predicted at a time in the location estimation system.

关键词： Indoor localization machine learning Sensor data Farm management Cow location

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Research progress of visual analysis of artificial intelligence application in depression field based on CiteSpace 4

Research progress of visual analysis of artificial intellige...

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4th international Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Zhang, Xin Chen, Zhencai Jiangxi Univ Chinese Med Key Res Lab Chinese Med Psychol & Brain Sci Jiangxi Prov Adm Tradit Chinese Med Grad SchColl Humanities Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798400708138

Depression is one of the most prevalent mental disorders with significant disability and mortality rates, which severely impairs the health and social function of individuals. To explore more efficient and objective diagnosis and treatment methods, the application of artificial intelligence technology in depression has gained increasing attention in recent years, providing a new tool for clinical practice and achieving preliminary successes. the goal of this study was to explore the research hotspots and frontiers of AI in the field of depression, providing reference for future research. Methods: We used literature related to AI in the field of depression retrieved from Web of Science as the data source. In combination with manual screening and information integration methods, CiteSpace software was used to make a statistical analysis of literature distribution, authors, institutions, keywords. Results: A total of 500 English articles were included, and the number of English articles increased significantly from 2019 to 2022. the authors were mainly new research groups in recent years. the pattern of institutional collaboration was relatively simple, and there were few highly cited articles. the research methods mainly involved data mining, machine learning, deep learning, and neural networks, while the application areas mainly focusing on the use of AI technology for predicting depression onset, early recognition, auxiliary diagnosis, and treatment planning. Conclusion: the research hotspots of AI in the field of depression are centered on improving depression intelligent methods, intelligent diagnosis, and intelligent prediction. the focus is more inclined towards intelligent prediction and diagnosis research. Research teams and institutions should strengthen cooperation among experts in different research fields to achieve multi-disciplinary integration.

关键词： artificial intelligence Depression CiteSpace software Visual analysis

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A Drug Sales Forecasting Method Based on Multiple Models and Parameter Optimization 4

A Drug Sales Forecasting Method Based on Multiple Models and...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Liu, Dawei Li, Tao Wuhan University of Science and Technology School of Computer Science and Technology Hubei China

ISBN: (纸本)9798350308082

Drug sales forecasting models can help chain pharmacies find potential users of targeted drugs. Affected by factors such as region, disease characteristics, user characteristics and drug types, it is difficult to make sales forecasts for specific drugs in different stores and in different granularity across the country. this paper presents a two-layer structure method for drug sales forecasting. It is optimized from two dimensions of model and parameter. Building a deep learning multi-model structure based on bagging allows different models to be used in different regions. An improved genetic algorithm was used to design the selection strategy and variation mode for drug marketing. SS-KSMOTE is designed based on k-mean, SMOTE and stratified sampling to solve the problem of sample sparsity. the experiment proves that the drug sales forecasting method under the two-layer optimization structure can effectively meet the drug sales forecasting needs under the complex scenario of chain pharmacies. © 2023 IEEE.

关键词： Deep learning

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Structured Approach for Relation Extraction in Legal Documents 4

Structured Approach for Relation Extraction in Legal Documen...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Sasidharan, Anjali K. Rahulnath, R. Department of Computer Science and Application Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. these techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain's rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. the resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain. © 2023 IEEE.

关键词： Clustering methods Graph reasoning Knowledge graph creation Legal documents machine learning Natural language processing Relation extraction

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Research on Detection Method of Large-Scale Network Internal Attack Based on machine learning 1

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Liu, Chang Long, Chaozhong Yu, Yuchuan Lin, Ziqi Jiangsu Fengren Information Technology Co. Ltd. Nanjing241000 China Kunshan Open University Kunshan215316 China Information Center of Public Security Bureau of Rongshui Miao Autonomous County Liuzhou545399 China Shandong Information Vocational and Technical College Weifang261044 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Aiming at the problem of inaccurate analysis of forged address attack behavior in current detection methods, this paper proposes a large-scale network attack detection method based on machine learning. A large-scale network internal attack detection model is constructed to analyze the degree of network internal attack. the model is trained by machine learning method to improve the convergence speed of model detection. After determining the attack target, analyzing the network vulnerability attribute, tracing the network internal attack source. Computes the hash of a function to ensure that the data set is complete and not tampered with. Alarm windows automatically check the point, set different alarm levels. the Euclidean distance is used as the distance measure of clustering process to distinguish the attack behavior. the detection formula of large-scale network internal attack is constructed, and the calculation result is divided into the level of large-scale network internal attack. Experimental results show that the method is consistent with the actual path, and the highest recall is 93%, the highest precision is 0.98, with good detection results. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Big Five Personality Prediction Based on Pre-training Language Model and Sentiment Knowledge Base 6

Big Five Personality Prediction Based on Pre-training Langua...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Lin, Hao Li, Xiaolei Tianjin University of Technology School of Computer Science and Engineering Tianjin300191 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China

ISBN: (纸本)9781510668546

machine learning-based personality prediction methods have become the mainstream prediction method. But existing Big Five personality prediction methods based on user-generated text rely too much on pre-trained language models to ignore the sentiment information. To tackle these issues, we propose a novel Big Five personality prediction method based on pre-trained language models and SenticNet6, which is a new sentiment knowledge base. the correlation analysis and ablation experiment shows that SenticNet6 can be used for Big Five personality prediction tasks. the experiment results indicate that our proposed method improves the state-of-art results for each Big Five personality trait by 9.89%, 9.39%, 2.99%, 3.01%, and 4.28% on Essays dataset, respectively. © 2023 SPIE.

关键词： Forecasting

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