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Deep Learning: A New Way of Looking into Fashion

Deep Learning: A New Way of Looking into Fashion

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Josh Hanley Thomas Roopashree S Jacob Charles Joy Haseeb Abdullah Kollarath Arun Siby Dept. CSE-AI and ML JAIN (Deemed-to-be University) Bengaluru India Dept. Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

Currently, Fashion-Fit using AI focuses on finding the perfect fit for users using GAN’s (General Adversarial Net¬works) and various methods proposed in the Artificial Intelligence Industry. However, due to changes in fashion trends and various preferences of the "type of fit" users want for their clothing the prior methodology is not meant for the perfect fit. These days a lot of people wear tighter or even get an oversized fit based on the user’s preference. In this paper we use Deep Learning techniques like CNNs and GANs to implement neural body fit by looking at various human body poses from large datasets and concatenating this data to make an accurate "desired" fit based on different human poses. This will set a new benchmark for virtual try-on. In the model being proposed, we are building Convolutional Neural Networks (CNNs) for body segmentation to make it easier to map a human pose. We will then map the human body using CNNs but not necessarily to find the "perfect fit". The CNNs will be used to make measurements that are similar to sizes available within the fashion industry allowing users to see how they would fit within those garments virtually. We will then use 2D renders, using uploaded pictures of the user from 4-5 different angles in order to allow the user to see how he/she would look in the outfit of their choice. These methods can give an accurate prediction helping every fashion e-commerce business. Personalized size and fit recommendations are critical for a fashion e-commerce business. Predicting the optimal fit increases consumer happiness while lowering expenses associated with size-related returns.

关键词： Industries Deep learning Biological system modeling Clothing Neural networks Size measurement Convolutional neural networks

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Optimization of Machine Learning and Deep Learning Algorithms for Diagnosis of Cancer 1

Optimization of Machine Learning and Deep Learning Algorithm...

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1st international conference on technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Krishna, Hari Anand, M. Saravanan, D. Pushpalatha, K. Department of ECE B V Raju Institute of Technology Telangana India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of CSE IFET College of Engineering Villupuram India Department of Mathematics KoneruLakshmaiah Educational Foundation Vadedeswaram India

ISBN: (纸本)9781607685395

Machine learning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in pattern recognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge data sets for medical pictures. An introduction to deep learning in medical image processing, from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a collection of computer science advances. The next step was to learn the basics of neural networks. That explains the use of deep learning and CNNs. So we can see why deep learning is rapidly advancing in various application fields, including medical image processing. The goal of this research was to use innovative methodologies on cancer datasets to explore the feasibility of combining machine learning and deep learning algorithms for cancer detection. This study used text and picture databases to classify cancer. The datasets are the Liver BUPA disorder database and brain MRI pictures. This article provides optimization methods that outperform the suggested approaches' accuracy. Using two alternative training methods, Levenberg Marquardt (lm) and Resilient back propagation (rp), two classification algorithms were evaluated with different groups of neurons to identify benign and malignant patients. Cascade correlation utilizing the train (rp) outperformed feed forward back propagation using the train (lm). The second deep neural network model presented a technique (based on CNN) for automated brain tumour identification using MRI data. The Water Cycle Algorithm is used to optimise CNN. The established approach is very accurate. The suggested framework examines innovative texture classification algorithms using Discrete Wavelet Transform (DWT) and the Gray Level Co occurrence

关键词： Artificial Neural Network Batch Normalization Deep Learning Gray Level Co-occurrence Matrix Water Cycle Algorithm

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Intelligent Network Architecture for Home Automation and Efficient Solution for Appliance Control in Smart Grid

Intelligent Network Architecture for Home Automation and Eff...

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international conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

作者： Abdullah Al Mamun Mehedi Hasan Jewel Parag Kumar Paul Saima Ahmed Rahin Department of Electrical and Electronic Engineering Bangladesh Army International University of Science and Technology Cumilla Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Electrical and Electronic Engineering United International University Dhaka Bangladesh

The field of “Internet of Things” (IoT) technology is exploding because of the quick advancements in automation technologies, embedded systems, and networking. It has become an integral part of Home Automation which is the combination of sensor networks, cloud computing, monitoring, and control. This paper discusses networking and provides two distinct methods for managing network-connected appliances as well as two different strategies for controlling the connected appliances and devices resulting in the enhancement of the system's robustness and overall stability. LoRa and 2.4GHz Wi-Fi technology have been used for the networking architecture of the appliances. Direct load control and phase angle-based voltage control methods are designed and developed for controlling the connected appliances. The distinct wireless technologies increase the stability of data transmission while ensuring minimum power consumption. AC voltage control methodology followed in this research also improves the robustness of the control systems. A mobile application platform is also developed and presented in this paper which is used to monitor and control the appliances, also known as sensor nodes.

关键词： Wireless communication Home appliances Wireless sensor networks Home automation Computer architecture Power system stability Robustness

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Preserving Mental Health Information in Speech Anonymization

Preserving Mental Health Information in Speech Anonymization

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international conference on Affective computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Vinesh Ravuri Ricardo Gutierrez-Osuna Theodora Chaspari Computer Science and Engineering Texas A&M University College Station TX USA

Widespread speech technologies may change the dynamics of mental health (MH) diagnosis, monitoring, and care by tracking spectrotemporal patterns of speech in a personalized manner. Yet, these technologies have given rise to a large public debate about the ability of users to protect their privacy. This work examines a speech anonymization algorithm that preserves information that is diagnostic of the MH condition of the speaker and representative of the phonological content of speech, while suppressing characteristics related to the speaker identity. The proposed anonymization algorithm relies on an auto-encoder that conducts an identity mapping between the original input, represented as the superposition of Mel-spectrogram coefficients and posteriogram (PPG) vectors, and the reconstructed output. The auto-encoder is trained in an adversarial manner to minimize the loss corresponding to MH-related information and maximize the loss corresponding to the identity (ID) of a speaker. The proposed algorithm is evaluated on data from healthy participants and patients with depression from the Distress Analysis Interview Corpus Wizard of Oz (DAIC-WoZ) dataset. Results indicate that the speech signals synthesized by the proposed anonymization algorithm have higher word error rate (WER), as calculated between the original speech transcripts and transcripts from the anonymized speech signals obtained via an automatic speech recognizer (ASR), and are rated as being less comprehensible by MTurk listeners compared to the original signals. However, the intelligibility of the synthesized speech appears equivalent to baseline speech anonymization algorithms that solely suppress the speaker ID without considering MH-related information, or rely on a cascade of signal processing methods that perform consecutive speech transformations. The proposed algorithm can further effectively suppress information related to the speaker ID, since the corresponding anonymized signals achieve 15.9%

关键词： data privacy Signal processing algorithms Speech recognition Mental health Signal processing Depression Information filtering

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Simulating the nanometric track-structure of carbon ion beams in liquid water at energies relevant for hadrontherapy 25

Simulating the nanometric track-structure of carbon ion beam...

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25th international conference on Ion Beam Analysis and 17th international conference on Particle Induce X-Ray Emission, IBA-PIXE 2021 and 23rd international conference on Secondary Ion Mass Spectrometry, SIMS 2021

作者： Vera, Pablo De Simonucci, Stefano Trevisanutto, Paolo E. Abril, Isabel Dapor, Maurizio Taioli, Simone Garcia-Molina, Rafael Universidad de Murcia Murcia30100 Spain School of Science and Technology University of Camerino Camerino62032 Italy Trento38123 Italy Trento Institute for Fundamental Physics and Applications Trento38123 Italy Departament de Física Aplicada Universitat d'Alacant Alacant03080 Spain Peter the Great St. Petersburg Polytechnic University Russia INFN Sezione di Perugia Perugia06123 Italy

The nanometric track-structure of energetic ion beams in biological media determines the direct physical damage to living cells, which is one of the main responsibles of their killing or inactivation during radiotherapy treatments or under cosmic radiation bombardment. In the present work, detailed track-structure Monte Carlo simulations, performed with the code SEED (Secondary Electron Energy Deposition), are presented for carbon ions in a wide energy range in liquid water. Liquid water is the main constituent of biological tissues, and carbon ions are one of the most promising projectiles currently available for ion beam cancer therapy. The simulations are based on accurate cross sections for the different elastic and inelastic events determining the interaction of charged particles with condensed-phase materials. The latter are derived from the ab initio calculation of the electronic excitation spectrum of liquid water by means of time-dependent density functional theory (TDDFT), which is then used within the dielectric formalism to obtain inelastic electronic cross sections for both carbon ions and secondary electrons. Both the ionisation cross sections of water by carbon ions and the excitation and ionisation cross sections for electron impact are obtained in very good agreement with known experimental data. The elastic scattering cross sections for electrons in condensed-phase water are also obtained from ab initio calculations by solving the Dirac-Hartree-Fock equation. The detailed simulations fed with reliable cross sections allow to assess the contribution of different physical mechanisms (electronic excitation, ionisation and dissociative electron attachment -DEA-) to the carbon ion-induced direct biodamage. © Published under licence by IOP Publishing Ltd.

关键词： Ion beams

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A Unified Image Repository for Cucurbits Diseases and Pests 1st

A Unified Image Repository for Cucurbits Diseases and Pests

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1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

作者： Adapa, Rithika Raksha Hemalatha, N. Pai, Prathamesh Sreekumar, K.M. Sajeesh, P.K. Kurian, Sainamole St. Aloysius Institute of Management and Information Technology Mangalore India Lcode Technologies Pvt. Ltd. Mangalore India College of Agriculture Padanakkad Nileshwar India

ISBN: (纸本)9783031756047

This study offers a user-friendly digital platform that hosts an integrated database of images encompassing diseases and pests that harm cucurbits plants. The database consists of two crucial aspects in cucurbits plants – pests and diseases. It allows users access to a vast image repository, encouraging research collaboration and facilitating quick responses for cucurbits crop protection. Our platform makes use of the strength of shared knowledge and cutting-edge technology to serve as a valuable resource for the agricultural community. The creation of this extensive image repository of cucurbits pests and diseases are described in detail in this study, with an emphasis on its importance for improving agricultural disease and pest control. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Collaborative platform Crop protection Cucurbits diseases Pest identification

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Alignment of Visual Senses and Acoustical Senses based on Emotion Recognitions

Alignment of Visual Senses and Acoustical Senses based on Em...

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international Symposium on Soft computing and Intelligent Systems (SCIS)

作者： Ja-Hwung Su Yi-Wen Liao Ming-Hung Kao Yung-Wen Tsai Chih-Jui Chang Hsiu-Wei Wu Cheng-Wei Chen Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Intelligent Commerce National Kaohsiung University of Science and Technology Taiwan

Recently, due to the rapid advances on multimedia technology, multimedia data such as pictures and music has heavily invaded our daily life. By the mobile camera, users capture the interested scenes as the preferred pictures, and then browse these pictures in either computers or mobile devices. This incurs an interesting issue that, how to provide the users with a better browsing/listening atmosphere and experience by music. Actually, it can be viewed as the problem for aligning photos with music and also as an exciting research topic for cross-media retrieval. However, very few studies focus their attention on this topic. To aim at this issue, in this paper, we propose a novel cross-media alignment method that bridges visual pictures to harmonic music by visual emotions and acoustical emotions. In this method, the pictures and music are projected onto the emotion space first. Next, the emotion features are fuzzed to be near the human senses. Finally, the effective alignment method for associating pictures with music is performed. The results of subjective and objective evaluations on the real datasets reveal that, our proposed method can successfully provide the users with a rich experience on audiovisual presentations. Also, the proposed alignment algorithm is shown to be effective in terms of Normalized Discounted Cumulative Gain.

关键词： Computers Bridges Visualization Emotion recognition Music Atmosphere Information retrieval

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A Descriptive Research on Internet of Things Implementation in Healthcare Sector

A Descriptive Research on Internet of Things Implementation ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Vivek Veeraiah G. K. Ravi Kumar Devesh Pratap Singh Akshita Chaudhary Ghada Elkady Dhyana Sharon Ross Department of Computer Science Adichunchanagiri University Mandya Karnataka India Department of Computer Science and Engineering Adichunchanagiri University Mandya Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Application RCCV Girls College Ghaziabad Uttar Pradesh India College of International transport & Logistics Arab academy for Science Technology & Maritime Transport Cairo Egypt Loyola Institute of Business Administration (LIBA) Chennai Tamil Nadu India

The Web of Things (IoT) is critical for the virtual change in medication because it provides innovative business models to facilitate and promote improvements in practice operations, control costs, enhance productivity, and improve patients’ experiences. The use of innovative IoT techniques in personalized health, however, remains incredibly challenging due to several issues, such as incoherent IoT program structures, diversity among connected wearable devices, a lack of accurate and affordable smart medical sensors, the complexity of the generated data, and an increased need for interoperability. This research delves at the history of IoT in healthcare, with a focus on how these technologies are being used to the rapidly expanding area of personalized medicine. It covers both common health scenarios and cutting-edge, intricate methods generated from the Internet of Things. It highlights the scientific, ethical, and economic challenges of developing a better medical system for early illness detection. At long last, the Web of Things (IoT) is permitting present-day medication to answer the requests rapidly of the time by creating a platform for communication across different health sectors, providing digital help at every stage, and so on.

关键词： Wearable computers Telemedicine Medical services Real-time systems Internet of Things Biomedical monitoring Medical diagnostic imaging

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FedComm: Understanding Communication Protocols for Edge-based Federated Learning

FedComm: Understanding Communication Protocols for Edge-base...

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IEEE international conference on Utility and Cloud computing (UCC)

作者： Gary Cleland Di Wu Rehmat Ullah Blesson Varghese School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK School of Computer Science University of St Andrews UK Cardiff School of Technologies Cardiff Metropolitan University Cardiff UK

Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exchange incurs a communication overhead and impacts the performance of FL training. There is limited understanding of how communication protocols specifically contribute to the performance of FL. Such an understanding is essential for selecting the right communication protocol when designing an FL system. This paper presents FedComm, a benchmarking methodology to quantify the impact of optimized application layer protocols, namely Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and ZeroMQ Message Transport Protocol (ZMTP), and non-optimized application layer protocols, namely as TCP and UDP, on the performance of FL. FedComm measures the overall performance of FL in terms of communication time and accuracy under varying computational and network stress and packet loss rates. Experiments on a lab-based testbed demonstrate that TCP outperforms UDP as a non-optimized application layer protocol with higher accuracy and shorter communication times for 4G and Wi-Fi networks. optimized application layer protocols such as AMQP, MQTT, and ZMTP outperformed nonoptimized application layer protocols in most network conditions, resulting in a 2. 5x reduction in communication time compared to TCP while maintaining accuracy. The experimental results enable us to highlight a number of open research issues for further investigation. FedComm is available for download from https://***/qub-blesson/edComm.

关键词： Performance evaluation Transport protocols Training Protocols Federated learning data models Time measurement

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Wireless Communications Network and Mobile computing using Blockchain in Distributed Internet of Things

Wireless Communications Network and Mobile Computing using B...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Ramapati Mishra Kapil Joshi Durgaprasad Gangodkar Institute of Engineering and Technology Dr. RML Avadh university Faizabad Uttar Pradesh Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (纸本)9781665487351

Electronic gadgets now work closely together as a result of the growth of the Internet of Things (IoT) and Mobile Edge computing (MEC). Strong dependability and integrity of the technologies are required. But the present trust procedures have the big drawback: (1) they significantly depend on a trustworthy third party, which might have serious security problems if it were compromised; and (2) they subject the associated devices to malicious assessments that may skew their trustrank. In this research, we propose a blockchain-based done to assure for global IoT devices that incorporates the ideas of financial planning and cryptography. The link prediction mechanism uses norms trust but also risk metrics to quantify trustrank, and a novel storage structure is created to make it easier for the domain secretariat to spot and remove fraudulent assessments of the objects. Evidence suggests that the suggested trust system can protect IoT devices from malicious assaults while also guaranteeing data exchange and consistency.

关键词： Wireless communication Measurement Multi-access edge computing Collaboration Computer architecture Financial management Market research

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