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3rd International Conference on Innovative Sustainab.e.Computational Technologies, CISCT 2023

作者： Shivahare, basu Dev Gupta, S.K. Katiyar, Alok Nath Punitha, S. Singh, Prabhishek Diwakar, Manoj Dr. APJ Abdul Kalam Technical University Lucknow India B.I.E.T Deptt. of CSE Jhansi India Amazon Web Services India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350303360

The process of medical imaging, which incorporates images produced by X-rays, ultrasound imaging, angiography, etc., is intricate. During the imaging process, image noise is also captured, some of which are very corrosive, causing a disturbance that impairs the quality of the image. The challenge of removing destructive Gaussian additive white noise from medical images while maintaining the fine features is addressed by the suggested approach. The concept of method noise is combined with a deep learning-based framework for a convolutional neural network (CNN), using method noise as a post-processing operation, to create the proposed technique. brain tumor detection over noisy images is much difficult with better accuracy. Hence in this paper we proposed a method for denoising as well as for brain tumor detection using CNN and U-Net. by explicitly adding Gaussian additive white noise at various noise variance values (= 10, 15, 20, 25, 30), distorted images are produced. The generated denoised images' aesthetic appeal and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), are next assessed. The results are also contrasted with those of other non-traditional methods to evaluate the effectiveness of the suggested methodology further. The findings of the critical examination demonstrate the proposed methodology's admirab.e.effectiveness in degaussing the medical images that had b.e. tainted by Gaussian noise. This method can be applied in a variety of real-world contexts in the realm of medical image processing. © 2023 Ieee.

关键词： Image denoising

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Private Document Vault with Server-Side encryption in Cloud AWS S3 bucket

Private Document Vault with Server-Side Encryption in Cloud ...

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International Conference on Communication and electronics Systems (ICCeS)

作者： Daliparthi Jeevana Aditya Sunki Laxmanraj R. Sathya bama Krishna Computer Science Engineering (B.E) Sathyabama Institute of Science and Technology Sath Sathyabama Institute of Science and Technology

The most requested future-oriented innovation in the world is distributed computing. Distributed storage is one of the most famous managements presented in cloud computing. Information is stored on various external servers as opposed to the fixed servers used in traditionally located information capacity in distributed storage. All the information stored on various external servers leaves the client unattended, and no one knows exactly where the information is stored. Rejected by distributed storage vendors who claim they can protect your information, but no one trusts them. Information stored via the cloud and traveling in cleartext design within an organization is a security risk. This research study proposes a strategy for allowing clients to securely store and access information from distributed stores. This technology ensures the security and protection of information stored in the cloud. Another advantage of this strategy is that it is encrypted using cryptographic methods. In this technology, the files are encrypted using both symmetric and asymmetric algorithms, which is nothing but Hybrid encryption and as a result the end user will get a pair of keys that will help in decrypting the files. If a data b.e.ch occurs the first layer of security will get damaged but the second layer of security helps in saving the files from data b.e.ch.

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estimating Anthropometric Soft biometrics:An empirical Method

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Intelligent Automation & Soft Computing 2023年第9期37卷 2727-2743页

作者： bilal Hassan Hafiz Husnain Raza Sherazi Mubashir Ali Yusra Siddiqi Engineering&Environment Northumbria UniversityLondon CampusLondonE17HTUK School of Computing and Engineering University of West LondonLondonW55RFUK School of Computer Science University of BirminghamBirminghamB152TTUK

Following the success of soft biometrics over traditional biomet-rics,anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/*** soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional ***,one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or *** level of accuracy is usually affected by several contextual factors such as overlapping body components,an angle from the camera,and amb.e.t *** and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or *** this purpose,anthro-pometric soft biometrics,which are originally geometric measurements of the human body,can be computed with ease and higher accuracy using landmarks information from the human *** this end,several key contributions are made in this paper;i)summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body,ii)a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field,iii)an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system,and iv)finally,a novel bag of anthropometric soft biomet-rics containing a list of anthropometrics is presented those are practically possib.e.to measure from an image or *** the best of our knowledge,anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained

关键词： Anthropometrics soft biometrics landmark localization estimation

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Ant Colony Optimization in MANeT for Decreasing energy Consumption with ML Approaches

Ant Colony Optimization in MANET for Decreasing Energy Consu...

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2023 Ieee International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Suresha, P. Math, Gayatri Arvind, Siddapuram bhaskar, M. Naveen, I.G. S.E.A College of Engineering and Technology Department of Mechanical Engineering Bengaluru India HKE Society's B.V. Bhoomaraddi College of Arts Science and Commerce Degree College Department of Electronics & Communication Engg Bidar India Hyderabad Institute of Technology and Management Department of Computer Science & Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

To coordinate access from the active nodes, MAC protocols are used in ad hoc networks. Many media access control (MAC) protocols have b.e. suggested for use in ad hoc networks, each with its own unique set of goals. The goal of many ad hoc networks is to increase the amount of time that its individual nodes, and by extension, the network, can function. Ad-hoc nodes are considered dead when their batteries run out, hence it is crucial to reduce power drain. because to the nature of MANeTs, in which nodes must depend on limited, portab.e.power sources, energy efficiency is a significant barrier to their widespread implementation. In multi-hop systems, where nodes must additionally expend energy to route packets for other nodes and assure the connection of the network, energy saving is difficult. Since energy-efficient routing is a relatively new area of study, there are still many open questions that need to be investigated and no widely accepted standards in place. Several routing protocols used the shortest route as a criterion for path selection;however, this is inappropriate due to the topology's significant and unexpected variability and does not assume the most efficient use of energy. Simulation is the primary tool for testing the efficacy of several routing protocols in MANeTs, including RIP, RIPng, PS-RIP, PSLLC-RIP, PS-RIPng, PSLLC-RIPng, FLbUDTCRIP, and ANFISbUDTCRIP. exata 5.4 GUI network simulator and OPNeT tools are used to analyse the efficiency of the suggested protocols. All the simulation results show \that the suggested protocols perform better than the current RIP protocols. © 2023 Ieee.

关键词： multi-protocol label switching

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brain Tumor Detection using MRI Image Processing Techniques

Brain Tumor Detection using MRI Image Processing Techniques

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2024 Ieee International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Jadhav, Ambaji S. Pawar, Rashmi Hattaraki, Sunilkumar M. bagali, Shwetha Patil, Rajeshwari S. Patil, bhagyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image preprocessing is done using CLAHe and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection. © 2024 Ieee.

关键词： Median filters

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Hand Talk: Sign Language to Text Converter using CNN 8

Hand Talk: Sign Language to Text Converter using CNN

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Shirisha, N. Jagannadham, D.b.V. Parshapu, Padma Singu, Sivanaga Malleswara Rao Kumari, V. Sravani Subhahan, D. Abdus Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India G.V.P College of Engineering Department of E.C.E Visakhapatnam India Hyderabad501510 India Vensai Technologies Inc. CummingGA30040 United States Godavari Institute of Engineering & Technology Department of Cse Rajahmundry India B V Raju Institute of Technology Department: Cse Narsapur502313 India

ISBN: (纸本)9798350376425

This research presents a novel system for converting sign language gestures into text, bridging the communication gap between non-sign language speakers and the deaf and dumb community. The system utilizes advanced image processing and machine learning techniques to accurately interpret and translate sign language. The proposed system employs a Convolutional Neural Network (CNN) classifier to process captured hand gestures and convert them into text. Through rigorous training and testing, the SVM classifier achieved a 96.7% accuracy rate for one-minute intervals. This research contributes to the development of accessib.e.and efficient sign language interpretation systems, facilitating communication and promoting inclusivity. by bridging the language barrier, this technology can enhance social interactions and improve the quality of life for individuals with hearing impairments. © 2024 Ieee.

关键词： Convolutional neural networks

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enhanced Non-Contact Vital Sign Monitoring via Depth Camera-Assisted Millimeter-Wave Radar

Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhang, Hanwen Dou, Chenglong Liu, Yucheng Wu, Yuan Qian, Liping Shi, Zhiguo Lu, Rongxing University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798331507886

Non-contact vital sign monitoring with mmWave radar has b.e. regarded as an effective method for identifying lung and heart diseases due to its strong penetration and non-invasive capabilities. Nevertheless, relying solely on millimeter-wave radar for vital sign monitoring is susceptib.e.to interference from multipath reflections, which degrades the measurement accuracy. To address this issue and enhance measurement accuracy, we propose a system design that leverages the depth camera for precise target location and sub.e.uently employs dynamic beamforming techniques to effectively monitor the specified region. The experimental results demonstrate that our proposed system achieves an average error of 2.44 beats per minute (bPM) for heart rate monitoring and 0.22 b.e.ths per minute (bPM) for respiratory rate monitoring. Compared to systems without beamforming, our system reduces the mean error for heart rate monitoring by 4.21 bPM and for respiratory rate monitoring by 0.51 bPM. © 2024 Ieee.

关键词： Heart

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brain Tumor Detector Using SVM Algorithm

Brain Tumor Detector Using SVM Algorithm

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International Conference on Computational science and Technology (ICCST)

作者： Aravintharaj. S Mithileshan. M b. Priya B.E Computer Science Engineering B.E Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu Computer Science Department Sri Sairam Engineering College Chennai TamilNadu

ISBN: (纸本)9781665476560

In the healthcare sector, disease prediction must be extremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. The brain is the most crucial organ of the human body since it regulates the operation of all other organs and aids in decision-making. It is basically the central nervous system's command post and is in charge of carrying out the body's regular voluntary and involuntary functions. The tumour is an uncontrolled proliferation of undesirab.e.tissue that has formed a fibrous mesh inside of our brain. The primary goal is to use this study to detect brain tumours at an initial stages. The study would provide a forecast indicating whether a tumour is present or not. This tool can be used as a significant asset to any medical facility dealing with brain Tumor. Using machine learning and deep learning algorithms enab.e. the detection of brain tumour's. We use the algorithm Support Vector Machine (a supervised machine learning approach that can be utilized for both regression and classification prob.e.s issues) out of all the machine learning algorithms since it has a greater precision and is simple to train on big datasets. In this study, the histogram of oriented gradient is used to collect the feature information from medical pictures of patients tumour's stored within the database. SVM is then used to separate these photos into tumour and non-tumor pictures. by using this method, it is possib.e.to predict brain tumour's quickly and with a high degree of accuracy (95%), which aids in patient treatment.

关键词： Support vector machines Machine learning algorithms Databases Scientific computing Magnetic resonance imaging Predictive models Classification algorithms

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Leveraging ensemb.e.Learning Model for Human Gait Recognition in Human computer Interface 2

Leveraging Ensemble Learning Model for Human Gait Recognitio...

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2nd International Conference on Automation, Computing and Renewab.e.Systems, ICACRS 2023

作者： Pant, Suman bhat, Niyati Shobana, G. Sindhuja, R. Lekha, D. Srimathi, S. School of CA & IT Shri Guru Ram Rai University India B.E Computer engineering University of Mumbai Mumbai India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur India Department of Medical Electronics Saveetha Engineering college India CSE Department R.M.K. College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Human Gait Recognition (HGR) is a biometric system that is exploited for security purpose over the past few years. The efficiency of gait detection is affected by different features namely walking surfaces, wearing clothes, and carrying a bag. Moreover, detection from divergent views is a complicated prob.e. in HGR. Numerous approaches were proposed for HGR using classical and DL methods but the conventional technique is not appropriate for massive data. This study leverages ensemb.e.Learning Model for Human Gait Recognition in Human computer Interface (eLMHGA-HCI) technique. The eLMHGA-HCI technique intends to recognize various kinds of human gaits to assist HCI applications. In the eLMHGA-HCI technique, Time Domain (TD) and Frequency Domain (FD) features from the input signals. For gait recognition, an ensemb.e.of three DL models takes place namely bidirectional Long Short Term Memory (biLSTM), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN). To exhibit the enhancedgait recognition results of the eLMHGA-HCI technique, a widespread simulation analysis is done. The simulation outcomes pointed out the enhanced performance of the eLMHGA-HCI approach over other existing techniques. © 2023 Ieee.

关键词： Activity recognition Deep learning ensemb.e.learning Gait recognition Human computer interface

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A Cross-Chain Interoperability Architecture for Smart City environments

A Cross-Chain Interoperability Architecture for Smart City E...

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2022 Ieee GLObeCOM Workshops, GC Wkshps 2022

作者： Amet, Matthieu Darshan, M. Srivastava, Gautam Crichigno, Jorge Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada Université de Lorraine Department of Computer Science Nancy France Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Brandon University Department of Mathematics and Computer Science BrandonMBR7A 6A9 Canada University of South Carolina College of Engineering and Computing Columbia United States

ISBN: (纸本)9781665459754

blockchains have become quintessential for cutting-edge technology demands in the world. This trend will only continue to increase in the future. Looking at all modern technologies that seem to move towards decentralized architecture, blockchain and distributed ledger technology fit perfectly. In times of conventional database-orientated systems, modern frameworks made huge developments when application programming interfaces (APIs) and data were used cross-platform between centralized entities. A natural evolution for blockchain technology would be to enab.e.communication and data exchange between blockchains (ie. both private and public). This could help not only digitize but could also set in a revolution for digital record-keeping that can be used for automation in the future. Our research work enab.e. and tests interoperability between blockchains in different real-world scenarios. The research work also tries to understand various elements of a smart city environment and a few use-cases are discussed and experimented on. © 2022 Ieee.

关键词： Interoperability

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