检索结果-内蒙古大学图书馆

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Gupta, Shilpi Sharma, Puranjan Gupta, Rajat Gupta, Neeraj Panipat Institute Of Engineering And Technology Deptt. Of Information Technology Panipat India

ISBN: (纸本)9798350306446

Air quality significantly impacts people's health, especially in indoor spaces like homes, schools, colleges, offices, etc. Even though air quality is essential, most educational campuses do not have a thorough monitoring system. As a result, staff and students may be exposed to health risks and unaware of the air quality they breathe. An Air Quality Monitoring System is desperately needed to improve indoor air quality. This paper aims to improve people's productivity and wellbeing in indoor environments by filling the current void in educational institutions' air quality monitoring. The suggested system helps to increase awareness of the significance of indoor air quality among staff, instructors, and students, in addition to acting as a real-time monitoring tool. Informed decision-making for preserving a safe and comfortable learning environment is made possible by analyzing the gathered data to find trends and patterns. The experiments were carried out at the PIET Campus in panipat. The system was checked at the home location for usage and accuracy. By putting this comprehensive air quality monitoring system into indoor spaces, especially educational institutions, we can proactively address any health risks related to poor air quality and make the learning environment safer and more comfortable for everyone. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potency of Neural Networks: A Thorough Examination of Deep Learning Techniques 1

Exploring the Potency of Neural Networks: A Thorough Examina...

引用

1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Tyagi, Manisha Panipat Institute of Engineering & Technology Haryana Panipat Samalkha132102 India

ISBN: (纸本)9798350392111

Deep learning stands as a revolutionary paradigm in artificial intelligence (AI), demonstrating its prowess across diverse areas, such as image recognition, natural language processing, and autonomous systems. This review article offers a comprehensive exploration of various deep learning techniques, shedding light on their distinctive features, applications, and recent advancements. From classic architectures to cutting-edge innovations, we delve into the diverse landscape of neural networks that have reshaped the field of artificial intelligence. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Potential of Artificial Intelligence in Healthcare Sector

Potential of Artificial Intelligence in Healthcare Sector

引用

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Khurana, Suman Malik, Ajay Narwal, Sonia Agarwal, Gaurav Sangwan, Kavita Panipat Institute Of Engineering And Technology Dept. Of Pharmacy Panipat India

ISBN: (纸本)9798350306446

Artificial intelligence (AI) is gaining attention in multidisciplinary area such as business, management, decision sciences, and healthcare sector. Nowadays AI is receiving more attention in health care sectors including diagnosis, patient data, and decision-making regarding treatment. This review article is based on the contribution of AI in healthcare and mainly focuses on AI in diagnosis, genomics, drug discovery and patient care. Along with the role of AI in health maintenance, it explains the various challenges faced in using AI in healthcare and disadvantages associate with its use. © 2024 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Blockchain and Autonomous Vehicles: Architecture, Security and Challenges 5

Blockchain and Autonomous Vehicles: Architecture, Security a...

引用

5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Aggarwal, Alankrita Gaba, Shivani Kumar, Jitendra Nagpal, Shally Panipat Institute of Engineering and Technology Computer Science and Engineering Haryana Panipat India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

In the smart culture where everything is going intelligently, there is a need to administer the intelligent transport system and improve the traffic congestion, accidents on road accidents, and above all suitable parking allocation. It is to be noted that intelligent vehicles are equipped with the internet that enables all types of communication with the surroundings. Automotive service providers like Uber and Ola are the inspiration behind Blockchain technology and Autonomous Vehicles. In some countries, they provide automated autonomous vehicles for smooth travel management and are known as linked or connected autonomous vehicles. Although such services are susceptible to attacks like men-in-the-middle attacks, parodying of a global positioning system, DoS attacks, sniffing, and many similar attacks, those are creating hurdles in the development of fully automated vehicles wherein the parallel autonomous industry is growing so fast as self-driving cars. Most of the studies concentrate on a central system with a single point of failure. The paper discussed about blockchain and autonomous vehicles, architecture, security, and challenges. © 2022 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Methodological Approach Integrating Deep Learning Techniques to Detect and Classify Anthracnose Disease for Sustaining Mango Leaf Health 7

A Methodological Approach Integrating Deep Learning Techniqu...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bhatia, Neha Kumari, Suman Kumar, Sandeep Panipat Institute of Engineering & Technology Department of Information Technology Haryana Panipat India

ISBN: (纸本)9798350350067

Mango, a large, cultivated fruit crop in India is the recent research area where research in detecting diseases found in mango leaves, fruits or stems is widely done. This is done to protect it as around 40% of crop gets destroyed due to pests and diseases. In this paper, study on one of the most popular mango leaf diseases, Anthracnose is done. The paper focuses on detecting the seriousness level of Anthracnose diseased mango leaf. These seriousness levels range from 1 to 10. A hybrid model consisting of fusion of CNN and Random Forest is proposed to extract the diseased part and classify it among seriousness level respectively. The suggested model is also distinguished with other pretrained models such as VGG16, CNN, ResNet50, and RNN to show its outstanding performance in predicting the seriousness level of Anthracnose. The efficiency at each seriousness level of the proposed method is evaluated through precision, recall and F1-score. The results indicated that precision ranges from 91.12% to 97.83%, recall measures from 91.32% to 99.27% and F1-score measures from 92.63% to 96.64% when calculated over all the classes. These outcomes exhibit the competence of the proposed CNN+RF model to categorize the seriousness level of anthracnose disease in mango leaf. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Estimation of permeability of soil using easy measured soil parameters: assessing the artificial intelligence-based models

引用

ISH Journal of Hydraulic engineering 2021年第S1期27卷 38-48页

作者： Singh, Balraj Sihag, Parveen Pandhiani, Siraj Muhammed Debnath, Sourav Gautam, Saurabh Civil Engineering Department Panipat Institute of Engineering and Technology Panipat India Civil Engineering Department National Institute of Technology Kurukshetra India Department of General Studies Jubail University College Jubail Industrial City Saudi Arabia

In this study, the support vector machine (SVM) technique was used to estimate the permeability of the soil. The performance of SVM model was equated with another type of artificial intelligence techniques such as Gaussian process (GP), random forest (RF) and multi-linear regression (MLR)-based models. While a GP, RF, and MLR model gives a good estimation performance, the SVM model outperforms them. Two kernel functions, Pearson VII and radial basis kernel function, were used in SVM and GP regression models. For this study, a data set contaning 95 observations collected from laboratory experiments. Out of 95, a total of 66 data sets were selected for preparing different algorithms whereas rest 29 data sets were selected to test the models. Input variables include percentage of sand (S), percentage of fly ash (Fa), specific gravity (G), time (T) and head (H), whereas coefficient of permeability (k) is considered as output. A comparison was also done with the various study, which shows poor relationship between the values of permeability. Sensitivity analysis concludes that the parameter, time and water head are the most effective parameters for the estimation of permeability of soil. © 2019 Indian Society for Hydraulics.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Intelligent Crop Selection and Soil Nutrient Management Using Machine Learning

Intelligent Crop Selection and Soil Nutrient Management Usin...

引用

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Sethi, Sujal Lakhina, Upasana Panipat Institute Of Engineering And Technology India

ISBN: (纸本)9798350306446

Machine learning employs a wide range of techniques to create models that can foresee yet-to-be-seen data in the future and derive prediction rules from known data, with the use of the same we generate a system that is being suggested to combine information from the meteorological service, grain depository, and soil data to anticipate the most suited crops based on the present state of the environment. The choice of the appropriate crop for the farm's soil conditions is one of the most crucial factors in agriculture. To aid these farmers, a Python prototype of a real-time crop recommendation programme that combines machine learning and data analytics was developed. The ability for computers to learn and adapt without explicit programming is provided by the field of machine learning, falls under the category of artificial intelligence (AI). Traditional disease detection and nutrient value estimation methods are being replaced by a new, more advanced approach that takes advantage of dataset upgrades and looks at the patterns different crops create in response to different climatic conditions. We are collecting data that includes values for the following elements: Nitrogen (N), Potassium (K), Phosphorous (P), Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn), Boron (B), and Sulphur (S) pH, temperature, humidity, rainfall, with the label of crop type for the various districts. High crop yields and the production of high-quality goods depend on the availability of soil nutrients. Therefore, the primary goal of this paper is to provide farmers with a remedy since they frequently administer fertilizer at random without realizing its poor quality or quantity. In order to produce new features, extract them, scale them for the specific ML model, and choose the best one, we provide and place our systematic dataset under numerous machine learning models. In light of this, automatic identification and monitoring are crucial for ensuring sustained agricultural income. © 2024 IEEE

关键词： Nutrients

来源：评论

学校读者我要写书评

暂无评论

EDFA gain flattening optimization with long period fiber gratings in WDM system

引用

Journal of Optical Communications 2022年第4期43卷 465-471页

作者： Gandhi, Divyangna Gupta, Shilpi Gambhir, Monika Svnit Ec Gujarat Surat India Sardar Vallabhbhai National Institute of Technology Eced Gujarat Surat India Panipat Institute of Engineering and Technology Ec Haryana Panipat India

This paper highlights a proposed optimized gain flatness technique for nonuniform gain spectrum of the erbium-doped fiber amplifier (EDFA) by introduction of long period fiber gratings (LPFG). In this analysis, EDFA gain spectrum has been evaluated between 1525 and 1600 nm with-20 dBm input signal power. Attenuation peaks of LPFG are optimized with a grating period of 240 μm and grating length of 30000 μm in the wavelength range of 1.1-1.8 μm, to compensate the peak gain of EDFA. Results have shown that EDFA peak gain of 35.94 dB is achieved at 1532.89 nm wavelength. This 35.94 dB peak gain is flattened up to 2.65 dB using long period fiber gratings. Also, less than ±0.3 dB gain flatness is achieved between 1528 and 1560 nm wavelength. The proposed less complex technique can be used to modify the grating parameters during fabrication in order to produce the loss peak at desired wavelength, which is efficient to flatten EDFA gain peak. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Erbium doped fiber amplifiers

来源：评论

学校读者我要写书评

暂无评论

A Proposed Method to Extend One-Bit of the Parallel Interface in a Digital to Analog Conversion Circuit by Adding an External Device

A Proposed Method to Extend One-Bit of the Parallel Interfac...

引用

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Shyan, Hatesh Manisha Singh, Kulvinder Chandigarh University India Panipat Institute Of Engineering & Technology Panipat India

ISBN: (纸本)9798350306446

In this paper, we propose a method to extend one bit to a parallel interface type Digital to Analog conversion circuit by using an external device. The examination of the Digital to Analog conversion circuit's principle is conducted in order to perform the aforementioned extension. Additionally, an analysis is carried out to identify the problems that arise when extending one bit using additional individual devices. Subsequently, a bit extension system for the Digital to Analog conversion device is proposed, utilizing an operational amplifier circuit. This method is utilising the high-precision characteristics of the operational amplifier, which affects the size of the output waveform only even when an error occurs in the device, and the voltage reversal phenomenon occurring between the bits doesn't take place. To verify the effectiveness of the method proposed, we constructed the circuitry for experiment and measured the absolute voltage and relative error of the output waveform. According to the experimental results, it was confirmed that only 0.0756% error occurred on average compared to the 12-bit class Digital to Analog converter. According to these results, we confirmed that one bit extension is possible by adding a small number of individual devices. © 2024 IEEE.

关键词： Digital to analog conversion

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Framework for Alzheimer's disease Classification Using EfficientNet Transfer Learning Model 4

An Intelligent Framework for Alzheimer's disease Classificat...

引用

4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022

作者： Sethi, Monika Ahuja, Sachin Singh, Sehajpreet Snehi, Jyoti Chawla, Mukesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Panipat Institute of Engineering and Technology Panipat India

ISBN: (纸本)9781665400732

Alzheimer's disease (AD) is a prevalent psychological disorder. The economic cost of treating for AD patients is expected to increase. Therefore in the last few years, research on AD diagnostic has laid great emphasis on computer-aided methods. The significance of developing an artificial intelligent diagnostic technique towards accurate and early AD classification seems essential. Deep-learning models hold significant benefits over machine learning approaches as these techniques do not require any kind of feature engineering. Moreover, T1-weighted Magnetic Resonance Imaging (MRI) is the neuroimaging data modality which is widely practiced for such a purpose. In some cases, the most significant barrier to integrating DL models into pre-existing applications is a lack of adequate data architecture. Changing medical information is usually hard to communicate, examine, and interpret. Transfer learning (TL) allows designers to use a combination of models in order to fine-tune a specified solution to a target problem. Transferring knowledge across two separate models could lead a generally a more reliable and precise model. In this work, researchers utilized an EfficientNet TL model already trained on ImageNet dataset to categorise subjects as AD vs. Cognitive Normal (CN) based on MRI scans of the brain. The dataset for this study was acquired from Alzheimer Disease Neuroimaging Initiative (ADNI). The performance parameters such as accuracy, AUC were used to evaluate the model. The proposed model on ADNI dataset achieved an accuracy level of 91.36% and AUC as 83% in comparison to other existing transfer learning models. © 2022 IEEE.

关键词： Magnetic resonance imaging

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：