检索结果-内蒙古大学图书馆

Polyhouse Agriculture with AI: Strategies for Climate Control, Energy Efficiency and Yield Improvement

SN Computer Science 2024年第8期5卷 1119页

作者： Mayuri, K.P. Kathavate, Sheela Niranjanamurthy, M. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management (Affiliated to VTU -Belgaum) Karnataka Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Karnataka Bengaluru India

artificial intelligence is applied to an extensive array of climate change responses, from data collection and classification to conservation decision-making and rule enforcement, leading to improved understanding and mitigation efforts. Greenhouse and polyhouse farming practices could significantly add to the carbon footprint, posing a challenge to be addressed if sustainable and environmentally friendly solutions are to be assured. This study describes the use of artificial intelligence in polyhouse agriculture to achieve better yields, save energy and advocate for sustainable farming. This study further elucidates the impact of AI-driven climate control systems on the quality of crops, resource use efficiency and productivity. The findings demonstrated the enormous potential of AI technology to improve plant growth, lower greenhouse gas emissions and increase sustainability in polyhouse agriculture. AI-driven climate control systems optimize the growing environment and reduce energy use with reduced emissions of GHGs, thus contributing to sustainable farming. However, this study also emphasizes the need for detailed research into the deployment and consequences of AI technology within polyhouse operations. The key bottlenecks are availability, security and regulatory compliance related to the generation of data. Therefore, need for a holistic approach to the design phase of AI-driven climate control systems in polyhouses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： AI-driven climate control Crop yield Greenhouse gas emissions Polyhouse agriculture Resource efficiency Sustainable farming

来源：评论

学校读者我要写书评

暂无评论

Improving Software Defects Detection: An In-Depth Analysis of machine learning Methods and Static Analysis Tools for Greater Accuracy 2

Improving Software Defects Detection: An In-Depth Analysis o...

引用

2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Bobade, Veer Puri, Chetan DMIHER Faculty of Engineering And Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India DMIHER Faculty of Engineering And Technology Department of Computer Science and Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, Software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results. © 2025 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

The Future of Driving: A Review on the Combination of artificial intelligence and Autonomous Vehicles 2

The Future of Driving: A Review on the Combination of Artifi...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Zade, Nikita Gawande, Mayur Verma, Prateek Gundewar, Swapnil Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The convergence of artificial intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Avoidance of Ship Collision Using Deep Reinforcement learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces 2

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Adnan, Myasar Mundher Sunil Kumar, V. Srinivas, T. Aditya Sai Aluvala, Srinivas Rajalakshmi Engineering College Artificial Intelligence & Machine Learning Department Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Review of machine learning and Deep learning algorithms for Personality traits classification 2

Review of Machine Learning and Deep Learning algorithms for ...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhadane, Satish N. Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits"It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of pe

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

From 5G to 6G: The Role of AI, machine learning, and Deep learning in Wireless Systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Transformative Trends in Diabetes Care: An Analysis of AI-Enhanced Predictive Healthcare 2

Transformative Trends in Diabetes Care: An Analysis of AI-En...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Milmile, Atharva Verma, Prateek Datta Meghe Institute of Higher Education and Research Du Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

This article examines the ever-changing environment of artificial intelligence (AI) in diabetes treatment. It investigates its existing uses, future developments, and the complex interaction of technology innovation, regulatory concerns, and ethical imperatives. AI is transforming diabetes management, from predictive analytics to tailored treatment regimens, by providing real-time information, increasing patient empowerment, and contributing to proactive, individualized care. The developing legal framework and emerging standards work to assure responsible AI technology development and deployment, addressing algorithmic biases and data privacy issues. The study forecasts future developments in the industry, imagining a collaborative future in which AI, seamlessly linked with other technology, augments healthcare professionals' competence and empowers people with diabetes. This investigation is guided by a synthesis of technological and ethical issues, highlighting the importance of inclusion, accessibility, and a harmonious human-AI relationship to advance the future of diabetes treatment toward better results and greater patient well-being © 2024 IEEE.

关键词： artificial intelligence Diabetes machine learning Predictive Healthcare

来源：评论

学校读者我要写书评

暂无评论

A Review on the Applications of Natural Language Processing in Healthcare 4

A Review on the Applications of Natural Language Processing ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Warbhe, Mohan K. Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards. © 2024 IEEE.

关键词： Challenges of NLP in Healthcare Future treads of NLP for healthcare Healthcare NLP

来源：评论

学校读者我要写书评

暂无评论

Automated Plant Disease Detection: CNN for Corn Maize, Tomato, and Potato 7th

Automated Plant Disease Detection: CNN for Corn Maize, Tomat...

引用

7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Angeline, R. Aruneshwaran, S. Department of Computer Science and Engineering with Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Ramapuram India

ISBN: (纸本)9789819720880

Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. This undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. The proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. The images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. The chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. These layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. The findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. The efficacious application of this model can assist farmers and agricultural specialists in inform

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

引用

2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：