International Journal of Food and Nutritional Sciences最新文献_第9页

A Deep Learning-based Approach for Colorization of Grayscale Images and Videos 基于深度学习的灰度图像和视频着色方法

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/194

Rama Devi Gunnam, Gurram Harini, Bapathu Anitha Reddy, Gurram Bhumika, Bikki Sai, Pavan Kumar

{"title":"A Deep Learning-based Approach for Colorization of Grayscale Images and Videos","authors":"Rama Devi Gunnam, Gurram Harini, Bapathu Anitha Reddy, Gurram Bhumika, Bikki Sai, Pavan Kumar","doi":"10.48047/ijfans/v11/i12/194","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/194","url":null,"abstract":"Colorization is the process of converting grayscale photos into colorful ones that are more visually appealing. Previously, a wide range of colorization techniques has been developed, which require the involvement of the human brain which consumes a lot of time and energy. In today’s world, there are many procedures that will automatically convert the grayscale image to a color image. Most of the conversion techniques incorporate elements of deep learning, machine learning, and art. This study gives a novel technique for coloring grayscale images that makes use of GAN and U-Net model characteristics. By using this technique, the model is able to learn how to colorize images from a trained U-Net. Additionally, the Fusion layer is used to combine the global priors for each class with the local information finds for each class, which are based on small image patches. This produces colorization outcomes that are more attractive on a visual level. Finally, the results of the method were obtained by doing an evaluation based on user research and comparing it to the state-of-the-art.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bird Species Identification using Audio Processing and AlexNet Neural Network 基于音频处理和AlexNet神经网络的鸟类物种识别

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/173

Sanjay Gandhi Gundabatini, Sangam Sai, Sri Vinay, Reddy, Thota Chandrika, Somarouthu Kaarthikeya, Pavana Kumaar, Shaik Siddik, Torlikonda Satya Akhil

{"title":"Bird Species Identification using Audio Processing and AlexNet Neural Network","authors":"Sanjay Gandhi Gundabatini, Sangam Sai, Sri Vinay, Reddy, Thota Chandrika, Somarouthu Kaarthikeya, Pavana Kumaar, Shaik Siddik, Torlikonda Satya Akhil","doi":"10.48047/ijfans/v11/i12/173","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/173","url":null,"abstract":"This research involved identifying the birds with the help of audio recorded from the real world environment. Most of the methods use images for detection of birds. But, some species may be similar to see. Hence, we took audio as the basis for classification. The audio frequency was plotted as spectrogram and it was inspected to extract the patterns and classify the bird. Legacy practices involved manual inspection of spectrogram that are plotted on the frequency of audio signals. But, this is time taking process and often produces inaccurate results. Hence, we created a computerized process to inspect the spectrogram. The computer learns from patterns of spectrogram during the training process and learns to detect a new and unseen audio. This entire procedure involved two crucial phases. The first stage of the process was to create a dataset with audio files collected from websites like Xeno-canto org that includes all sound recordings of birds. In this research work, we considered 4 species of wood pecker in the Germany region. Hence, we have collected approximately 120 recordings for each species, thus a total of 500 recordings were collected. The collected sounds undergone a series of pre-processing phases like reconstruction, framing, and silence removal, pre-emphasis for removing any noises like human actions, wind sounds, tree sounds. For every processed sound clip, the spectrogram is plotted and it was given as input to a neural network that is in the second stage, which in turn detects the recording at the end. Since the image was given as input, we used Convolutional Neural Network (CNN) which is a best neural net in deep learning for text and image based tasks. The CNN categorizes sound clip and determines the species of bird based on input features. A model was created and put into practice.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Smart Grid Equipment Diagnosis through Blockchain and ABE 通过区块链和ABE实现安全智能电网设备诊断

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/200

Y. Divya, Tarun Devanaboyina, Shaik Mohammad Mustaq, Tata Anirudh

引用次数: 0

Drowsiness Detection Using Haar and CNN Algorithm 基于Haar和CNN算法的睡意检测

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/187

M. Naga, Sri Harsha, M. Saketh, K. S. Teja, K. S. Sri, K. Shravani

引用次数: 0

ESTIMATING BODY MASS INDEX FROM FACIAL IMAGES 根据面部图像估计身体质量指数

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/182

Dr. O. Aruna, M. V. N. L. S. B. Sri, R. Jyothirmai, P. C. Sri, M. M. Teja, N. V. Krishna

引用次数: 0

Image Steganography Using Modified DWT Technique 基于改进DWT技术的图像隐写

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/212

N. BrahmaNaidu, Kandimalla RamaKrishna, Keerthi Diyyala, Manne Sai Swaroop, Kolla Sandeep

引用次数: 0

Segmentation and Classification of Dermatoscopic Skin Lesion images using U-Net and MobileNet models 基于U-Net和MobileNet模型的皮肤病变图像分割与分类

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/188

引用次数: 0

Liver Tumor Segmentation using Deep Learning Techniques 基于深度学习技术的肝脏肿瘤分割

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/170

Mr. A. Vishnu Vardhan, Kondamudi Swetha, KaipaRajeswara Reddy, Kesana Sainadh, Mahanth Nannapaneni

引用次数: 0

Parkinson’s Disease Detection Using Deep Learning 利用深度学习检测帕金森病

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/220

K. Sireesha, M. Lahari, N. A. Prasad, N. K. Teja, R. S. Shivani

引用次数: 0

Medical Image Fusion for Brain Tumor Detection 医学图像融合在脑肿瘤检测中的应用

International Journal of Food and Nutritional Sciences Pub Date : 2023-04-05 DOI: 10.48047/ijfans/v11/i12/210

S. Narendra, A.Nikhitha, A.Pardha Saradhi, B.Mohan, Krishna Ajay Kumar, Krishna Chowdary

{"title":"Medical Image Fusion for Brain Tumor Detection","authors":"S. Narendra, A.Nikhitha, A.Pardha Saradhi, B.Mohan, Krishna Ajay Kumar, Krishna Chowdary","doi":"10.48047/ijfans/v11/i12/210","DOIUrl":"https://doi.org/10.48047/ijfans/v11/i12/210","url":null,"abstract":"Medical image fusion is an important task in medical diagnosis that aims to provide a complete representation of medical images by combining multiple imaging modalities. The fused image is then fed into deep learning algorithms for tumor classification. In this paper, we propose an approach for medical image fusion of CT and MRI scan images for brain tumor detection. This work proposes an algorithm for the fusion of several imaging modalities, such as MRI and CT, based on a classifier with a fusion rule. Several qualitative and quantitative evaluation metrics have been used to assess the performance of the proposed method and compare it to cutting-edge image fusion techniques. On the basis of metrics like standard deviation, entropy, mutual information, etc., the experimental findings are assessed. In terms of accuracy and training loss metrics, the experimental results show that the proposed approach outperforms the individual modalities. As a result, the suggested technique can be employed as an effective and accurate instrument for the detection of brain cancers. The method can be used to increase diagnosis precision and decrease the false-negative rate, which will ultimately improve patient outcomes.","PeriodicalId":290296,"journal":{"name":"International Journal of Food and Nutritional Sciences","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121079560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0