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TSNE+K-Means Based Heritage Classification System

TSNE+K-Means Based Heritage Classification System

引用

2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Chen, Hao Qian, Pengfei Lv, Lin Zhang, Guoheng College of Electrical Engineering Northwest Minzu University Gansu Engineering Research Center for Eco-Environmental Intelligent Networking Lanzhou China

ISBN: (纸本)9798350359244

Heritage is a microcosm of history and possesses much historical and practical value. Aiming at the current problem of relics classification relying on experience and no obj ective scientific classification, this paper proposes a relics classification model based on TSNE+K-means, which provides a relics classification method based on machine learning, and it has been verified that the method has a good classification effect in the classification of glass relics and basically matches the existing classification methods. © 2023 IEEE.

关键词： Machine learning

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PAIR: Protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: Protein-aptamer interaction prediction based on langua...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Jun Yan, Zhiqiang Zeng, Hao Zhu, Zexuan Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions. © 2024 IEEE.

关键词： Contrastive learning

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Integration of Deep learning Models and Explanatory Models to Detect Anomalous Periods in Vital Signs of Mining Truck Engines 31

Integration of Deep Learning Models and Explanatory Models t...

引用

31st IEEE international conference on Electronics, Electrical engineering and computing, INTERCON 2024

作者： Lima, Ines Katia Huaman Gladys Cutipa Arapa, E. Professional School of Computer and Systems Engineering National University of San Antonio Abad de Cusco Cusco Peru

ISBN: (纸本)9798350378344

'Latex The Peruvian mining industry is beginning to adopt IoT technologies. The increasing complexity of modern engines makes it crucial to implement predictive maintenance systems to ensure reliability and performance optimization. This study is based on data obtained from an open-pit copper mine in southern Peru. The use of advanced machine learning models is proposed, evaluating between Long Short-Term Memory with Autoencoders (LSTM-AE) and Artificial Neural Network (ANN) for anomaly detection in multivariate time series for diesel engine health monitoring. This project addresses three issues of the current analysis methodology: first, point anomalies are used to monitor potential failures, generating a high rate of false alarms, while the proposed method identifies abnormal time periods;second, the threshold for alerts is manually calculated and requires expert intervention periodically, whereas the proposed methodology calculates the threshold in an unsupervised manner. Third, deep learning models are often considered 'black box' models since it is not easy to interpret how the model reached a conclusion, which is why model explainability is important for its adoption within the industry. The objective is to develop a predictive model that identifies potential failures early, allowing for the planning of preventive maintenance and reducing unplanned downtime. The results show that the combination of these models significantly improves the accuracy in failure prediction, facilitating timely and precise interventions. © 2024 IEEE.

关键词： Predictive maintenance

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Forecasting Cloud Workload through Reinforcement learning-Based Ensemble Model 4

Forecasting Cloud Workload through Reinforcement Learning-Ba...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Kang, Yuliang Li, Yongke Zhang, Gongxuan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798331534622

The escalating demand for cloud computing has intensified the need for accurate workload forecasting to optimize resource allocation and maintain service quality. This paper presents a novel approach employing a reinforcement learning-based ensemble model to enhance the accuracy of workload predictions in cloud environments. The study addresses the challenges posed by complex and dynamic workload patterns, leveraging deep learning techniques to improve prediction outcomes. The proposed model dynamically selects the optimal subset of predictors, demonstrating superior performance on the Google Cluster dataset. The research aims to contribute to more efficient and intelligent resource management in cloud computing. © 2024 IEEE.

关键词： Prediction models

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A Machine learning based Smart Grid for Home Power Management using Cloud-Edge computing System

A Machine Learning based Smart Grid for Home Power Managemen...

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2023 international conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Kavya, B.M. Sharmila, N. Naveen, K.B. Mallikarjunaswamy, S. Manu, K.S. Manjunatha, S. Ks Insititute of Technology Dept of Electronics and Communication Bengaluru India Jss Science and Technology University Dept of Electrical and Electronics Engineering Mysore India Engineering Bgs Institute of Technology Dept of Electronics and Communication Nagamangala India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India Don Bosco Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Global Academy of Technology Dept of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350306927

In recent years, a huge amount of data has been communicated between internet of things (IoT) devices. Apart from these dependencies, many fields such as smart grids, smart cities, and smart homes are incorporating IoT devices. This work focuses on refining issues faced with existing power grids, such as unidentified fault detection, prediction of power generation, and utilisation at the consumer side of existing power grids. To overcome this problem, a machine-learning-based smart grid using computing systems is designed to control renewable energy power generation, power prediction, fault detection, and utilisation at the consumer side. This is achieved using cloud-edge computing. As per the simulation analysis, the proposed work shows improved performance in comparison to convolution neural network (CNN) based cloud computing with respect to power utilisation, throughput, and fault detection of 0.25%, 0.12%, and 0.35%, respectively. © 2023 IEEE.

关键词： Edge computing

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ACO-Based Hyperparameter Tuning of a DL Model for Lung Cancer Prediction 5

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cance...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rao, D. Nageswara Rathee, Manisha Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350383522

Machine learning models could provide human intelligence to perform complex tasks effectively. Among them, deep learning models have successfully been applied in several domains. However, that efficacy can be achieved only through effective design and training of deep learning models, which is a complex task to achieve and requires a lot of expertise in the domain. Finding the optimal hyperparameter values is crucial for high-performance machine learning models, especially tree-based models and neural network-based models. Estimation of the parameters such as the weight of neural networks can be done using data sets and these are internal model parameters, however the hyperparameters such as number of layers, size of layers, learning rates etc. are external to the model. The values of hyperparameters can either be determined intuitively by domain experts or by manually and randomly trying various possible configurations until we arrive at some acceptable values. Both have severe limitations for machine learning models with a lot of hyperparameters. Several attempts have been made in the direction of automatic hyperparameter optimization to save human efforts in designing machine learning models. Nature-inspired computing has gained a lot of popularity in this direction in recent years. In this paper, we have applied ant colony optimization, a nature-inspired approach for hyperparameter optimization for deep learning models for the prediction of lung cancer. © 2024 IEEE.

关键词： Ant colony optimization combinatorial optimization Deep learning model Hyperparameter optimization Metaheuristic

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20th international conference on computing in Civil and Building engineering, ICCCBE 2024

20th International Conference on Computing in Civil and Buil...

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20th international conference on computing in Civil and Building engineering, ICCCBE 2024

ISBN: (纸本)9783031842238

The proceedings contain 34 papers. The special focus in this conference is on computing in Civil and Building engineering. The topics include: Data-Based Assessment of the Resilience of Municipal Road Infrastructure as a Component of Sustainable Asset Management;Where Project Governance and Sustainability Meet? A Review of Literature and Discussion on IPD Governance Structure;analysis of Software Solutions to Find the Most Suitable Project Delivery Model;Multi-source Transfer learning Method for Enhancing the Deployment of Deep Reinforcement learning in Multi-Zone Building HVAC Control;2D and 3D Thermal Transition Presentation of Temperature Spikes in Net-Zero Energy Residential Test Facility;a Driver Business Model: Triple Layer Business Canvas Model for Integrating Drivers of the Energy Efficiency Market in Europe;a Knowledge Graph-Based Approach for Construction Safety Hazards Management and Rectification Measures Intelligent Recommendation;Hazards Identification and Impact Reduction for At-Risk Construction Activities Using 3D BIM and VR;recurrent Neural Network-Based Workers’ Posture and Workload Estimation on Building Construction Sites;implementation of Wearable Technologies for Enhancing Safety Monitoring at Construction Jobsites: A Trend Analysis;unveiling the Spectrum of Personal Heat Stress Responses in Buildings;analyzing the Effect of Wind on Construction Schedules;image-Based Traffic Data Collection in Traffic-Related Air Pollution Analysis;prediction of Expansion Joint Life in Concrete Pavement;asset Management of Municipal Road Infrastructure;impact of Municipal Referencing Basis on Methods of Road Condition Assessment;employing the Policy Gradient Approach for Strategic Decision-Making in Road Network Infrastructures;classification of Physical Fatigue Levels in Shoveling Using Heart Rate, Electrodermal Data, and Skin Temperature.

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Revolutionizing Healthcare Intelligence Multisensory Data Fusion with Cutting-Edge Machine learning and Deep learning for Patients' Cognitive Knowledge

Revolutionizing Healthcare Intelligence Multisensory Data Fu...

引用

2024 international conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Swamy, Samatha R. Nandini Prasad, K.S. Dept. of Ise Bengaluru India Dept. of Ise Bengaluru India

ISBN: (纸本)9798350359688

The study explores the recent advancements in healthcare that have witnessed an unprecedented integration of multisensory data, ranging from wearable devices to medical imaging and electronic health records. This paper explores the transformative impact of cutting-edge machine learning algorithms and deep learning algorithms in processing and interpreting this multisensory data, offering unparalleled insights into patients' cognitive well-being. The review delves into the types and challenges of multisensory data, evaluating performance of supervised and supervised machine learning models for disease detection and pattern recognition respectively. Additionally, it scrutinizes the importance of deep learning applications that are based on convolutional neural networks as well as recurrent neural networks for medical image classification. The paper also mentions the importance of the integration of multisensor data fusion with these advanced algorithms, emphasizing real-time monitoring systems and ethical considerations. Summaries from research data shows effectiveness of remote patient care and predictive analysis and personalized treatment plans. The exploration of future directions and opportunities underscores the prime importance for further advancements in explainable AI, collaborative learning, and edge computing, consolidating the vision of a healthcare paradigm revolutionized by intelligent data fusion and cutting-edge technologies. © 2024 IEEE.

关键词： Electronic health record

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Machine learning Based Hardware Accelerated Cobot Grasping in the Food Industry 11

Machine Learning Based Hardware Accelerated Cobot Grasping i...

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11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Ivačko, Nikola Ćirić, Ivan Ćojbašić, Žarko Milošević, Maša Jevtić, Dušan University of Nis Faculty of Mechanical Engineering Nis18000 Serbia Fazi doo Belgrade11000 Serbia

ISBN: (纸本)9798350386998

In food industry automation, the integration of machine learning with robotic systems can introduce improved efficiency and precision in various tasks such as fruit and vegetable handling and manipulation. This paper introduces an innovative approach to robotic grasping, specifically customized for the challenge of identifying and handling apples and oranges within a dynamic and unstructured environment. Utilizing the YOLO (You Only Look Once) object detection algorithm, the system consists of a camera mounted on the robot's end effector, enabling real-time identification and localization of fruits and vegetables. Implemented on the NVIDIA Jetson platform, this solution presents the combination of machine learning techniques with hardware acceleration, ensuring optimal performance in real-time object recognition and manipulation tasks. Our experimental setup tests the robustness of our object recognition and localization approach under varying lighting and orientation scenarios and evaluates the system's overall performance in a real-world, cluttered environment typical of the food industry. The findings of this study underscore the potential of integrating ML-powered vision systems with hardware-accelerated robotics for food handling and manipulation applications. By demonstrating high levels of accuracy in recognition, classification and grasping, our research contributes to the broader field of robotic automation, offering insights into the scalability and adaptability of such systems across different sectors of the food industry. © 2024 IEEE.

关键词： Fruits

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Accelerating Sustainability through Leveraging Machine learning to Analyze CSR Spending in the Indian Automobile Industry 7

Accelerating Sustainability through Leveraging Machine Learn...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Yogi, Kottala Sri Dankan Gowda, V. Pati, Anwesha Srinivas, Ved Aghav, Santosh Abdullah, Ibrahim International University Symbiosis Institute of Business Management Hyderabad Symbiosis Department of Operations Pune India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Symbiosis Law School Hyderabad Pune India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350372816

This paper uses machine learning to assess Corporate Social Responsibility spending trends on the Indian automobile sector between 2016 and 2021. Simply put, the aim of the research is to recognize trends and patterns in sustainability expenditures. More specifically, the study examines how businesses allocate money across the three important aspects of the sustainability report: People, Planet, and Profit Purcell, 2020. The research is based on a largely industry-based database obtained from public companies' records. The use of advanced statistical techniques, such as machine learning and random forest modeling, enables one to examine the impact of financial allocations on sustainability achievements. Increments in CSR spending are related to enhanced company sustainability performance in terms of correlation outcomes. The paper also recognizes areas where cost cuts have the best returns, as well as shortfalls in the existing CSR approach, offering a strategy for possible development's. © 2024 IEEE.

关键词： CSR Impact Sustainability Analysis Indian Automobile Industry learning Machine Social Responsibility Environmental Spending Sustainability Investments Corporate

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