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Predicting Schizophrenia from fMRI Using Deep Learning 1

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3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kardani, Shail Sharma, Raghav Sharma, Abhishek Deparment of Computer Science Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Deparment of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Schizophrenia is a complex and multifaceted disorder that is still not fully understood. However, many processes are known to drive its onset, relapse, symptomatology, and therapy. One possible explanation for Schizophrenia is that the person has difficulty using proper analytic methods to deal with the complexity and variety of the condition. Many AI algorithms have been designed and developed over time specifically for Neuroinformatics. These algorithms have been a go-to for many researchers and scientists, even for those in the medical field. Because of these AI-powered algorithms, Scientists are able to detect various diseases, whether they are related to brain activity or any other organs. Deep Learning is yet another field of Machine Learning which is made by the inspiration of how neural networks are connected inside our brain. These are more flexible than the Scikit-Learn-based Machine Learning models as these Deep Neural Network models don’t often require data preprocessing at initial stages. Not only this fact, but also they are used in various places, for example, processing of unstructured data, including texts and images, feature extraction, etc. In this study, we have attempted to detect Schizophrenia from resting-state functional magnetic resonance images using trained Deep Neural Networks as well as Scikit-Learn-based state-of-the-art Machine Learning models. For the dataset, we have utilized the public COBRE dataset available in the nilearn library, along with some public datasets from OpenfMRI. The Deep Learning models detected Schizophrenia with a reliable performance compared to Scikit-Learn-based Machine Learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance

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Lesion Isolation from Apple Plant Leaves Affected by Black Rot Disease using Optimized Masking on Various Color Channels

Lesion Isolation from Apple Plant Leaves Affected by Black R...

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Aryan Gupta Aditya Sinha Shaleen Poddar Computer Science Manipal University Jaipur Jaipur India

Apples were harvested on 308,000 hectares of land in 2020, according to the United Nations Food and Agriculture Organization, making it one of the most popular fruits in the country. According to the Economic Times, India's Apple imports more than doubled in 2021. Apples can only be grown in select parts of India, such as Jammu and Kashmir, Himachal Pradesh, Uttarakhand’ s hills, and Uttar Pradesh's plains. Botryosphaeria obtuse is the fungus that causes Apple black rot. On leaves, infections start as reddish-brown specks that grow into circular brown lesions (4–8 mm in diameter). The segmentation of the Apple black rot disease would benefit greatly from the lesion isolation or masking technique developed thus far in this project. After the ROI has been extracted, further processing can be done in the form of disease categorization, disease stage, correlation between the behaviour of different diseases, and so on. This will aid in the selection of better chemicals, insecticides, fertilisers, and agricultural techniques in general, reducing illness, increasing output, and increasing profit.

关键词： Economics Correlation Image color analysis Computational modeling Organizations Lesions Jamming

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State-of-the-Art Methods for Fine-Grained Emotion Detection from Malayalam Text using Deep Learning: A Survey

State-of-the-Art Methods for Fine-Grained Emotion Detection ...

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international conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： K Anuja P C Reghu Raj Remesh Babu K R Dept. of Computer Science and Engineering Govt. Engineering College Palakkad A. P. J. Abdul Kalam Technological University Kerala India

Social media has changed the way people express their opinions, views, and feedback about anything in their minds. This large volume of sentiment-rich data needs to be examined properly in order to comprehend the mood, attitude, or sentiment underlying those words. computers can process, interpret, and analyze vast volumes of natural data using natural language processing (NLP), which helps them to comprehend human languages. Now all organizations are looking for more fine-grained sentiment analysis to plan their business strategy. This paper investigates state-of-the-art textual data analysis using NLP techniques. The increased use of Indian languages in communicating platforms is a challenging task for opinion mining. In this paper, we survey the state-of-the-art methods based on deep learning and fuzzy rules for sentiment analysis.

关键词： Deep learning computers Sentiment analysis Emotion recognition Data analysis Social networking (online) Mood

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Image Encryption and Decryption Algorithm Based on Fractional Order Simplified Lorenz Chaotic System and DNA Coding

Image Encryption and Decryption Algorithm Based on Fractiona...

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international conference on Electronics, Communications and Information Technology (CECIT)

作者： Deguo Yang Xiaohui Liu Yaru Gao College of Computer Science & Engineering Northwest Normal University Lanzhou China

Traditional information encryption techniques lead to inefficient encryption and affect image formats when encrypting images. Therefore, how to ensure the secure storage and transmission of digital images has become a great challenge in the current image processing research. Chaos, a relatively typical nonlinear system at present, has many advantages and meets the design requirements of image encryption systems. In this paper, we take the basic theory of chaos as the entry point and combine it with DNA coding operation to investigate the image encryption method. A new color image encryption and decryption algorithm are designed based on the research of scholars. Mainly, DNA encoding is performed first, followed by a permutation operation to solve the chaotic sequence, generate a DNA matrix and perform operations using DNA operation rules; finally, DNA decoding is performed to obtain the ciphertext image. In addition, to decrypt the image, the pixel average of each component is hidden in the last pixel of each component matrix, which further improves the security of the algorithm. The simulation experimental results show that this method of encryption and decryption works well.

关键词： Image coding Chaotic communication Digital images DNA Color Encryption Information and communication technology

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IoT Architectures for Indoor Radon Management: A Prospective Analysis 3rd

IoT Architectures for Indoor Radon Management: A Prospectiv...

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3rd international conference on Intelligent Edge processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

作者： Blanco-Novoa, Oscar Barros, Paulo Fraga-Lamas, Paula Lopes, Sérgio Ivan Fernández-Caramés, Tiago M. Department of Computer Engineering Faculty of Computer Science Universidade da Coruña A Coruña15071 Spain Centro de Investigación CITIC Universidade da Coruña A Coruña15071 Spain ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo4900-348 Portugal CiTin - Centro de Interface Tecnológico Industrial Arcos de Valdevez4970-786 Portugal IT - Instituto de Telecomunicações Campus Universitário de Santiago Aveiro3810-193 Portugal

ISBN: (纸本)9783031359811

The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2 ), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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A Robust Image Dehazing Model Using Cycle Generative Adversarial Network with an Improved Atmospheric Scatter Model 33rd

A Robust Image Dehazing Model Using Cycle Generative Adversa...

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33rd international conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Guo, Xinlai Tao, Yanyun Zhang, Yuzhen Xu, Biao Zheng, Jianyin Ji, Guang Soochow Univ Sch Rail Transportat Suzhou 215005 Peoples R China Minist Educ Key Lab Informat Proc & Intelligent Control Shanghai 200240 Peoples R China Key Lab Digital Signal & Image Proc Guangdong Pro Shantou Peoples R China Soochow Univ Affiliated Hosp 1 Suzhou 215006 Peoples R China Suzhou City Univ Suzhou 215104 Peoples R China Beijing TeleSound Elect Co Ltd Beijing 100089 Peoples R China Suzhou Transportat Big Data Innovat & Applicat La Suzhou Jiangsu Peoples R China

ISBN: (纸本)9783031723377;9783031723384

Learning-based dehazing methods have attained numerous achievements in dehazing images. However, during the dehazing, the image depth and the atmospheric scattering factor are completely ignored. Current models result in the poor quality in dehazing real-world haze images. In this study, we propose a robust image dehazing model (RIDehaze) based on cycle generative adversarial network (CycleGAN). In RIDehaze, a dehazing GAN leverages a transmission map estimator with an improved atmospheric scattering model to restore clear images, which contain more real-world physical characteristics. CycleGAN utilizes depth estimator to generate more realistic haze images to train the dehazing GAN, thereby improving its generalization. Meanwhile, considering that the discriminator in CycleGAN struggles to support network training in the later stages, we designed a multi-level discriminator. This design ensures that even in the later stages of training, the discriminator can still effectively guide the network in dehazing images. On the real-world haze images of the RESIDE and Haze4k datasets, RIDehaze obtains clearer and more natural haze-free images and can specially produce better visual effects of distant scenery. On the synthetic hazy images, RIDehaze obtains similar PSNR and SSIM to supervised learning methods and outperforms the other unsupervised learning methods. The code for the method in this article can be downloaded at "GitHub address", where the GitHub address is a hyperlink to: https://***/Paris0703/icann_RIDehaze/tree/main.

关键词： Single Image Dehaze CycleGAN Unsupervised learning Image Depth Multiple Discriminator

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A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments 3rd

A Classification Framework Based on Multi-modal Features fo...

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3rd China Intelligent Robotics Annual conference, CCF CIRAC 2022

作者： Chen, Sheng Xie, Haiqun Yang, Hongjun Fan, Chenchen Hou, Zengguang Zhang, Chutian Institute of Automation Chinese Academy of Sciences Beijing100190 China The First People’s Hospital of Foshan Guangdong528010 China Macau Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9789819903009

Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer’s disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the effect of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Personalized Emotion Detection from Text using Machine Learning

Personalized Emotion Detection from Text using Machine Learn...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： Reema Goyal Navneet Chaudhry Mandeep Singh Department of Computer Science and Engineering IMS Engineering College Ghaziabad UP Department of Computer Science and Engineering Chandigarh University Gharuan Punjab India University Institue of Computing Chandigarh University Gharuan Punjab India

ISBN: (纸本)9781665499453

Social media platforms have evolved into essential tools for sharing emotions with the rest of the globe. People utilize text, photos, music, and video to express their emotions or their points of view. On the Internet, these social media platforms generate a vast volume of unstructured data. All the existing emotion detection models based on the supervised learning methodology require to collect labelled data which is time- consuming task. Next, for languages that are underutilized in terms of NLP, such as Polish vocabulary. To tackle these issues, this paper presents a model that functions as an emotional awareness system. It is based on open forums with automated audio information and text analysis for user-based emotion detection. The model is a key component of many natural computer programmers for processing natural language. The proposed model is implemented using PocketSphinx as an ASR. In addition to this, the model also uses the Word2Vec model, K-means clustering, and the Term frequency-inverse document Vectorizer for text analysis and segmentation. Further, the data from the international Survey on Emotion Antecedents and Reactions (ISEAR) was used to test and train the model. Finally, the concept evolves into a user-based system that will act as a personal assistant for segregating people's emotions as they experiment with new apps. The ISEAR database will be used for performance testing. When utilized in the database, the suggested model greatly outperforms previous models, with an accuracy of 80.25 percent and an F1 score accuracy of 88.86 percent. As the future work, this algorithm can be integrated into an application where emotion is used for recommendation.

关键词： Emotion recognition Analytical models Vocabulary Text analysis Social networking (online) Databases Text recognition

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Blind Source Separation of Color Noisy Blurred Images 23rd

Blind Source Separation of Color Noisy Blurred Images

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23rd international conference on Computational science and Its Applications, ICCSA 2023

作者： Biondi, Giulio Boccuto, Antonio Gerace, Ivan Department of Mathematics and Computer Science University of Perugia Perugia Italy

ISBN: (纸本)9783031371165

Blind Source Separation (BSS) is a relevant problem in the signal processing research area, with a broad application sphere. In this work, we consider a BSS problem with recto–verso images subject to show–through and bleed–through effects;in particular, we suppose that the observed image is obtained using a two-step linear process: the recto and verso images are separately blurred using a blur operator with known coefficients and then mixed. Our algorithm, Blind Estimation Technique Imposing Smoothness and Non–Overlapping (BETIS–NO), built on top of previous work, computes the entries of the mixture matrix, which regulates the intensity of recto–verso mixing for the document reconstruction;the introduction of second–order discontinuities and additional constraints on source images, i.e., minimum entropy, non–Gaussianity, and correct overlapping, leads to improved quality of the reconstruction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blind source separation

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LSIP: Locality Sensitive Intensity Projection for 2D Mapping of High-Res 3D Images of Dendritic Spines 3rd

LSIP: Locality Sensitive Intensity Projection for 2D Mappin...

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The 3rd international conference on Frontiers in Computing and Systems (COMSYS 2022)

作者： Paul, Shauvik Das, Nirmal Dutta, Suchandra Bose Adityar, Rayala Chakraborti, Tapabrata Zeug, Andre Basu, Subhadip Department of MCA Techno Main Saltlake Kolkata700091 India Institute of Engineering and Management Kolkata700091 India Computer Science and Engineering Department Jadavpur University Kolkata700032 India Department of Medical Physics and Biomedical Engg and Cancer Institute University College London London United Kingdom The Alan Turing Institute London United Kingdom Cellular Neurophysiology Hannover Medical School 1 Carl-Neuberg Street Hannover30625 Germany

ISBN: (纸本)9789819926794

Biological objects at numerous scales such as tissue and cells are commonly used for study for a long time. Acquiring 3D fluorescence microscopic images and applying image processing algorithms to the acquired images are frequently used to study these biological objects. Working with 3D images is computationally expensive and difficult. Maximum intensity projection, frequently used process for 2D mapping of the 3D image, generates a disconnected layer of voxels, creating important artifacts and wrong interpretations. Here we present a method Locality Sensitive Intensity Projection(LSIP) that produces a continuous focused 2D mapping from a 3D volume while maintaining local spatial relationships. We illustrate the utilities of the proposed method by applying it in the 2D mapping of 3D confocal microscopic images of dendritic spines. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fluorescence microscopy

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