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2022 International Conference on Advances in Computing, Communication and Materials, ICACCM 2022

作者： Rawat, Vandana Singh, Neelam Kaur, Bhavleen Bora, Saksham Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9781665474399

Presently hydroponic methods of growing different varieties of crops are acquiring acceptance in different parts of the world. It is a technique of growing plants with the help of water. Traditional methods of farming are facing different types of threats like lack of farming lands, expansion of urban areas, change in climatic conditions and extensive use of chemical fertilizers and toxicants which is exhausting the fertility of the soil and transforming cultivable lands into barren lands. With the tremendous growth of population nowadays it is quite difficult to supply all the human race with an adequate amount of food by growing them on the basis of traditional methods. The farming land is also changing in residential areas to meet the demand of the growing population in order to provide them a place for living. As a result, different methods of farming need to be adopted. In this study, hydroponic methods are explained with the help of machine learning algorithms. This system works on the principle of KNN and Lasso Regression algorithm to automatically provide them with nutrients dissolved in water with the help of roots. With the help of this technique, we can overcome the issues of food scarcity by growing an ample number of crops of different types, all year round. Hydroponics is a technique for the cultivation of plants without using soil. Earlier this method was practiced manually by keeping an eye on the nutritional needs of plants by maintaining a balance of various factors such as acidity or basic (pH), electrical conductivity (EC), temperature and the value of total dissolved solids(TDS). In this research Machine, Learning Methods are used that help us to measure pH, TDS, and nutrient temperature value in the nutrient film technique (NFT) by using some sensors. In this research, tomato is an object of an experiment, and Comparison is done using Machine Learning Algorithms to speculate the categorization of nutrient conditions for it. © 2022 IEEE.

关键词： Hydroponics

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Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection 11

Using Classifier with Gated Recurrent Unit-Sigmoid Perceptro...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Sikand, Rainy Singh, Harveer Chandigarh School of Business Department of Master of Computer Application Jhanieri India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

The method for classifying a picture using the CNN algorithm and various activation functions is the subject of this research study. From a human perspective, there are several sorts of situations found in birds, such as various sizes, altitudes, colours, and forms. There is a more substantial variety in photos to recognize the species of bird than there is in the accepted audio categorization of birds. Additionally, people have a really tough time identifying birds from photos. The system is suggested to support the concepts of identifying a portion of the birds and extracting the CNN characteristics using several convolutional layers in order to discriminate and categorise the birds. These characteristics are then gathered and presented to the decoder at that time for classification. Various bird photos and activation functions are used to test the CNN method. The nodes of the picture are checked to the different testing data sets, such as cifar-10 and Berkeley Birds 200, with order to determine their correctness. Python is used to create the suggested system in either Omnibus 3 or Dragonfly 3 programs. According to the experiment results and analyses, CNN with activated Re LU-Sigmoid outperforms CNN with the other activation functions in terms of accuracy. © 2022 IEEE.

关键词： Chemical activation

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Exploring the Potential of Transfer Learning for Accurate Segmentation of Pathological Structures in X-Ray Images 2

Exploring the Potential of Transfer Learning for Accurate Se...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kishore, Lalit Shaikh, M.Z. Goel, Pawan Kumar Badhoutiya, Arti SRMIST Delhi Ncr Department of Computer Application UPGhaziabad Modinagar India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

This paper explores the capability of transfer learning for the correct segmentation of pathological systems in X-ray pictures. Switch mastering is a way in gadget mastering wherein understanding received from a source challenge is transferred to an extraordinary goal task to enhance performance. This paper specializes in how transfer getting to know may be applied to segmenting pathological systems in X-ray photos. The paper discusses the proposed technique's overall performance on datasets from public medical resources gathered from healthy and diseased lungs and stomach organs. The experiments used in this paper display that with the resource of pre-educated models in transfer getting to know, the performance of the segmentation of the pathological regions in X-ray photos may be stepped forward. Eventually, the paper outlines the ability of transfer mastering for clinical picture segmentation and discusses how it can be further progressed in the future. © 2024 IEEE.

关键词： Healthy Pathological Potential And Medical Transfer

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Distributed Aperture Coherent Synthesis Method based on Two-stage Compensation 2

Distributed Aperture Coherent Synthesis Method based on Two-...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Liu, Zhongwei Zhou, Dongming Xiang, Rongrong Chen, Jie Wu, Tianhang Ding, Tianyu Liu, Xiaochuan Gao, Hongwei Beihang University School of Electronic and Information Engineering Beijing China School of Information and Electronics Beijing Institute of Technology Radar Research Lab Beijing China Beijing Institute of Computer Technology and Application Department of Data Technology and Application Beijing China Beijing Institute of Radio Measurement Radar R&d Center Beijing China

ISBN: (纸本)9798331515669

To solve the problem that it is difficult to accurately estimate the coherent parameters of distributed aperture radar under the condition of low signal-to-noise ratio, a distributed coherent synthesis method based on two-stage compensation is proposed in this paper. This method utilizes prior information such as system layout, beam pointing, and inertial navigation station location to perform coarse alignment of echoes at the first stage in time, and then uses cross-correlation method to estimate coherent parameters of delay and phase within a small range, and perform precise compensation of time and phase at the second stage, so as to realize the coherent synthesis of multiple radar echoes. Experimental results show that the proposed method can effectively realize distributed coherent synthesis processing under low signal-to-noise ratio. © 2024 IEEE.

关键词： Signal to noise ratio

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Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty

arXiv

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arXiv 2024年

作者： Li, Haoxuan Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Engineering Research Center of Advanced Computer Application Technology Ministry of Education School of Computer Science and Engineering Beihang University Beijing100191 China

Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender systems. Graph convolutional network framework would confuse the semantic of uncertainty in the nodes, and similarity measured by Kullback–Leibler (KL) divergence suffers from degradation problem and demands an exponential number of samples. To address these challenges, we propose a novel approach, called the Wasserstein dependent Graph ATtention network (W-GAT), for collaborative filtering with uncertainty. We utilize graph attention network and Wasserstein distance to learn Gaussian embedding for each user and item. Additionally, our method incorporates Wasserstein-dependent mutual information further to increase the similarity between positive pairs. Experimental results on three benchmark datasets show the superiority of W-GAT compared to several representative baselines. Extensive experimental analysis validates the effectiveness of W-GAT in capturing uncertainty by modeling the range of user preferences and categories associated with items. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Bharat Tripathi Madan Lal Saini Manish Jain Apoorva Saxena Narendra Pal Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids. learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion.

关键词： Hair Privacy Neurons Signal processing algorithms Signal processing Hearing aids Fall detection Transient analysis Recruitment Microphones

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Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surge Wire Grades Ring Modification was Achieved 11

Utilizing the Fem Approach, PSO, and Firefly Algorithm, Surg...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaur, Amandeep Singh, Harveer Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

As well as an arrester, semi power and electromagnetic current distributions have been seen, which impacts the dependability and lifespan of the device. With the HV load resistor, a rating ring is used to provide an even spread of the electric field. The Finite Element Technique (FEM), a tried-and-true approach, is used to assess the effects of sorting ring characteristics. It contributes to extending the useful life of surge arrester ZnO blocks under extreme stress. Optimizer (PSO) and BAT techniques are used for validation. © 2022 IEEE.

关键词： Optimization

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition 1

A CNN-Based Framework for Detection and Recognition of Objec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Tiwari, Shivam Sharma, Vikrant Rathore, Yogesh Singh Gandhi, Parul Srivastava, Durgesh Sharda University Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Sharda University Computer Science and Application UP Greater Noida India Manav Rachna International Institute of Research and Studies Department of Computer Applications Haryana Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350338423

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species. © 2023 IEEE.

关键词： Feature extraction

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Texture-Based Garments Defect Detection Method Using Machine Learning 2nd

Texture-Based Garments Defect Detection Method Using Machine...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Rahul, Mayur Verma, Shekhar Singh, Akhilesh Verma, Sarvachan Yadav, Vikash Department of Computer Application School of Engineering & Technology CSJM University Uttar Pradesh Kanpur India Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Technical Education Government Polytechnic Bighapur Unnao Uttar Pradesh Manpur India

ISBN: (纸本)9789819735938

Fabric defects identification is significantly important in textile industry. Various activities are involved in detecting fabric quality. The total profits of the industries depend on the defects on the quality of fabrics that produces loss. Existing defect identification methods are conducting in different organizations with the help of human defect inspectors who draw manually defect pattern. However, these detection techniques have few drawbacks like complication, inaccuracy, negligence, tediousness, and exhaustion in addition to time-consuming which causes decrease performance in failure finding. For the sake of overcoming these problems, a framework that depends on processing of image has been introduced to efficiently and effectively identify the defects of fabrics. The given introduced work consists of image segmentation, morphological methods, feature extraction, and classification. We have used edge detection for image segmentation, ICA for feature extraction, and SVM for image classification. The results achieved from the given framework are superior than existing framework. The experiments have been conducted in Python environment using TILDA datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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Deep Learning-Based Diagnostic Tool for Brain Tumours 3

Deep Learning-Based Diagnostic Tool for Brain Tumours

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Singh, Arun Rakhra, Manik Singh, Dalwinder Sharma, Arvind Kumar, Rakesh Lovely Professional University Department of Computer Science & Engineering Jalandhar Phagwara India School of Computer Application Lovely Professional University Punjab Phagwara India Lovely Professional University Department of Mathematics Punjab Phagwara144411 India

ISBN: (纸本)9798350303360

In this work, we provide the results of our experiments on Brain Tumour Classification and explore their implications. MRI, short for magnetic resonance imaging, is a crucial piece of medical imaging technology that creates high-resolution pictures of the inside of a human body by combining radio waves, a strong magnetic field, and a computer. In this study, we employ a CNN-based Deep learning model to categorise cases of brain tumours. Common applications for CNNs (Convolutional Neural Networks) include video and image analysis. It processes images by first ingesting them and then convoluting them into smaller and smaller pieces. The convolutional layer analyses the input picture using a series of filters designed to identify elements like edges, curves, and patterns. Here, we constructed the CNN using data obtained from Kaggle. It contains roughly 3300 MRI scans of the brain, split into 4 groups (no tumours, meningioma tumours, glioma tumours, and pituitary tumours) and a total of 384 testing pictures. We used Keras to construct the model. The accuracy of the suggested project is quite high, coming in at 97.142%. The Confusion matrix is used to evaluate the precision. It is a data table for measuring the efficacy of an ML system. This provides the results of a comparison between the actual values in a dataset and the model's projected values in an easy-to-understand format. © 2023 IEEE.

关键词： Neuroimaging

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