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A Data-Efficient Grasping Framework for Robust Grasp detection Under Sparse Annotations

A Data-Efficient Grasping Framework for Robust Grasp Detecti...

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Networking systems of AI (INSAI), international conference on

作者： Gaoxiong Lu Siao Liu Zile Wang Tongyu Liu Wei Li Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

Recently, robotic grasp detection has achieved excellent performance thanks to the deep neural network (DNN). However, previous work primarily focuses on improving the detection accuracy when the models are trained on datasets with sufficient annotations, which is hard to meet in real scenarios. Some dataefficient grasp detection methods are proposed to reduce labeling costs but perform poorly on novel objects. Besides, experimental results demonstrate that existing grasping methods suffer from severe performance degradation once the number of annotations is reduced. To address the above issues, we propose a dataefficient grasping framework called Coarse-Segmentation Guide GrapsNet (CSGG) for sparse annotation scenarios. Specifically, our method builds an auxiliary segmentation task in a weaklysupervised way to learn the generic grasping prior for grasping novel objects without exogenous data. In addition, an IoU-guide loss is devised to alleviate the performance degradation caused by the extreme imbalance between foreground-background information. Extensive experiments show that our CSGG outperforms the state-of-the-art grasping methods on two challenging benchmarks (Cornell and Jacquard) with various annotation accessible ratios. In a physical robot arm, we achieve a 92.1% success rate when the model is trained on a subset of the Cornell dataset.

关键词： Degradation Costs Accuracy Annotations Grasping Artificial neural networks Benchmark testing Manipulators Labeling Artificial intelligence

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Data Security system in Maintaining the Medical Records Using Wireless Network Cryptography Management Integrated with the Artificial intelligence

Data Security System in Maintaining the Medical Records Usin...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Rahul Bhatt Hayder Mahmood Salman Vikas P. Kaduskar Geethamanikanta Jakka Shibani C Aich Devvret Verma Department of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science Department Al-TurathUniversiy College Baghdad Iraq College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune India Data Analytics Information Technology University of the Cumberlands USA Department of English Amrita Vishwa Vidyapeetham Amritapuri India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

Therefore, we will study here. One of the technological fields in the communication industry that is actively developing and increasing the fastest is wireless technology. Interconnection is the ability to communicate without the usage of cables, wires, or maybe another removable drives. This network architecture, which focused on offering timely action versus numerous problems, is able to evaluate the info and practically through and cellular networking and it will protect the health records from possible threat by keeping it. In order to protect material from possible harm, encrypted methods have been employed extensively throughout the years. Using communication devices may help increase the information is available protection. In this way, only authorized persons may view the information. Ensuring that now the data is secure from threats, and no one else can. A variety of sensors are used in wireless sensor systems to measure environmental factors and transmit information to a central site. Originally designed for defense use, these sensor nodes are now extensively used in a wide range of business and commercial situations in addition to cutting-edge industries like medicine. Wireless sensor networks for remote health monitoring certainly improve the standard of care today. There are two frequent security flaws in healthcare applications: dropping and impersonating.

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Breast Cancer detection On X-Tray Mammogram Images

Breast Cancer Detection On X-Tray Mammogram Images

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Muhammad Arrif Amirul Azmi Wan Azani Mustafa Hiam Alquran Azwa Abdul Aziz Muhammad Naufal Mansor Laith H. Alzubaidi A. H. A. Hussein Faculty of Electrical Engineering Technology Universiti Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh Padang Besar Perlis Malaysia Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images.

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Deepflow: A Software-Defined Measurement system for Deep Learning

Deepflow: A Software-Defined Measurement System for Deep Lea...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Prasanna Kumar Lakineni Saurabh Kumar Sanjay Modi Kapil Joshi V. Mareeskannan Jayapal Lande Department of CSE GITAM School of Technology GITAM University Visakhapatnam Invertis Institute of Computer Applications Invertis University Bareilly Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University Dept. of CSE St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

Delivering perfectly alright real-time traffic information is crucial for managing a wide range of networks, particularly vehicular communications, anomaly analysis, networking accounting, and available bandwidth. Application networking might be able to give fine-grained evaluation by offering details for each sent rules of just an Open circulation switching. Providing absolutely adequate real-time traffic information in hardware switches also poses serious problems because of the size constraints of TCAMs that can only accommodate a minimal number of rules in contrast to the number of current fluxes in the networks. Inside this editorial, we initiate Intense Flow going, a scheme for modular app assessing that's also premised on an efficient method that a) flexibly senses the channel's highest traffic references and locations prefixes, b) collects coarse-grained stream size readings for less energetic identifiers and perfectly alright metrics for the more engaged users; c) includes historical metrics to coach a cloud-based a profound learners model that has the potential to create short forecasts anytime precise f Due to the lack of the need for additional flow sampling methods that compromise accuracy, a large increase in the number of totally acceptable flows that may be recorded is now possible. . Deep Flowing can provide incredibly high accuracy for estimating flow quantities at various hierarchy levels, according to a rigorous experimental analysis using a prototype versions and actual networking signals.

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Advances in Clustering Algorithms for Large-Scale Data Processing with AI

Advances in Clustering Algorithms for Large-Scale Data Proce...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Sonal Sharma Vandana Singh Jeyalaxmi M Prasad B. Chaudhari Neeraj Das Avni Garg Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

Clustering is a widely used technique in statistics mining for exploring and reading records with AI. It is an unsupervised gaining knowledge of technique, which may be used for exploratory facts evaluation. Clustering primarily aims to group similar statistics factors in a given facts set. In recent years, diverse advances in clustering algorithms have been developed for massive-scale record processing with AI. Some of the maximum popular clustering algorithms are ok-means, hierarchical clustering, fuzzy clustering, and spectral clustering. This kind of algorithm was used with AI for massive-scale records processing. Okay-approach and hierarchical clustering algorithms were widely used in diverse information mining responsibilities. They permit one to discover and organize the maximum similar statistics points. Fuzzy clustering and spectral clustering algorithms have become famous recently because of their capability to duplicate human perception and questioning when clustering records. Lately, deep learning techniques have been used with clustering algorithms to enhance their overall performance and boom the accuracy of records evaluation. Deep mastering is a subset of machine mastering which allows computers to research from repeated exposure to big datasets robotically. It permits clustering algorithms to investigate big-scale facts faster and more as it should be. Clustering with AI is an effective method for processing and analyzing huge-scale records. Recent advances in clustering algorithms and deep mastering strategies have improved performance and accuracy. Moreover, these algorithms may be used in diverse applications, which include client segmentation, community evaluation, and anomaly detection.

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Mask R-CNN with R-D Net: A detection and Segmentation Model for Early Gastric Cancer Gastroscopy Images

Mask R-CNN with R-D Net: A Detection and Segmentation Model ...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Tao Liang Yi Lei Wenai Song Xue Sun Jun Du Qing Wang Li Zhao School of Software Engineering North University of China Taiyuan China Faculty of Information Technology Beijing University of Technology Beijing China VIP Department and Family Medicine Department Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing China Department of Pathology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing China Pharmacovigilance Research Center for Information Technology and Data Science Cross-Strait Tsinghua Research Institute Xiamen China Department of Automation Tsinghua University Beijing China Department of Gastroenterology Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Chinese Academy of Medical Sciences Beijing China

Gastric cancer is one of the most common malignant tumors of the digestive system. Although gastroscopy can effectively detect early cancers, manual identification and diagnosis is time-consuming and laborious, and the false-negative rate is as high as 4.6-25.8%. Early gastric cancer lesion areas can be detected and segmented using deep learning techniques. We collected and organized 12665 eligible gastroscopy images, and cropped and expanded the original dataset after labeling by professional doctors, and finally obtained 19673 images for model training. We propose ResMSE Net and DWFF Net and apply these two networks to Mask R-CNN to obtain a new model: Mask R-CNN With R-D Net. On the same standard dataset, the mAP@[IoU=0.5] and ACC values of the Mask R-CNN With R-D Net model achieve respectively 45.37% and 67.74%, which are improved by 5.83% and 9.08%, respectively, compared with the original model. In our experiments, we found that Mask R-CNN With R-D Net is difficult to detect targets that are extremely similar to the background, which is the direction of our future work. Secondly, we hope to minimize the number of model parameters and make the model more lightweight under the premise of ensuring accuracy, so that the model can be embedded into gastroscopy detection equipment for real-time detection.

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Smart security system using Hybrid system with IoT and Blockchain: A security system Human sased detection

Smart security system using Hybrid System with IoT and Block...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Priyank Singhal Sachin Sharma Supratim Saha Isa Mishra Badria Sulaiman Alfurhood Prabhdeep Singh College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India ECE Swami Vivekanand Subharti University Meerut KIIT School of Management KIIT Deemed to be University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

By eliminating the need for information rehashing, blockchain technology enables the secure transmission of sensitive data without the need for duplication, reducing patient history errors and saving time. The information is made even more secure by the fact that it has a timestamp. Blockchain technology has the potential to improve financial transaction security and efficiency in a variety of healthcare applications. This prevents unauthorised individuals from seeing or altering the patient's medical information in addition to restricting access to it. In this study, it is suggested that using blockchain technology to connect patients and medical professionals to a mechanism for data retrieval and storage would aid in the development of a secure and effective healthcare system. Since 1970, there have been numerous token-based methods, including passports, bank cards, bank accounts, identification cards, and keys; however, none of them have been successful in developing a reliable and secure conduit for transactions. Personal Identification Recognition Numbers (PIRN), passwords, and other security measures are used in conventional knowledge-based systems. Since they are so easily lost, stolen, or misplaced, they often can't maintain confidentiality or establish the legitimacy of a claimant. Additionally, since usernames and PINs are so easily forgotten or guessed by a fraudster, personally identifiable information like this is particularly vulnerable to theft. Due to its inherent potential and advantages over other traditional techniques of identification and verification, biological identification and authentication, also known as biometrics, is currently garnering a lot of attention. Particularly in the context of information security systems, this is true. The device's distinctive biological properties, such as fingerprints, facepalms, gesture recognition (including iris), and the device's own iris, enable a wide range of applications. These consist of forensics,

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Cable Defect detection Based on Terahertz Time-Domain Spectroscopy technology

Cable Defect Detection Based on Terahertz Time-Domain Spectr...

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Computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE international conference on

作者： Guofang Luo Qi Liu Qiyu Lu Hongtao Chen Xi Chen State Grid Shanghai Electric Power Research Institute Shanghai China State Grid Shanghai Municipal Electric Power Company Shanghai China Huirui Optics Technology (Beijing) Co. Ltd. Beijing China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Cables have good insulation performance, small footprint, and high transmission safety, making them an important component of China's power transmission and transformation system. If cables are damaged or broken due to certain factors, it often leads to power outages in a certain area. Therefore, analyzing the causes of cable faults and making accurate judgments can accelerate the monitoring and prevention of faults, and reduce open circuit losses. The use of terahertz technology can perform non-destructive testing on the interior of cables, but the direct imaging results of terahertz systems have problems such as large size, unclear, poor quality, low contrast, and noise. In this article, wavelet denoising is used to process the image, and the results show that wavelet denoising can effectively remove noise and improve image contrast. Terahertz technology can provide a new approach for cable defect detection and improve the accuracy of defect detection.

关键词： Terahertz wave imaging Spectroscopy Noise Noise reduction Terahertz materials Circuit faults Time-domain analysis

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An Effective Framework for Gastrointestinal Disease detection Using Hybrid Features 3rd

An Effective Framework for Gastrointestinal Disease Detectio...

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3rd international conference on Machine intelligence and Smart systems, MISS 2023

作者： Sharmila Joseph, J. Vidyarthi, Abhay Singh, Vibhav Prakash VIT Bhopal University Bhopal-Indore Highway Madhya Pradesh Sehore466114 India Motilal Nehru National Institute of Technology Prayagraj Allahabad211004 India

ISBN: (纸本)9783031691140

Machine Learning (ML) techniques are widely used for the detection and classification of diseases in medical fields. These algorithms make decisions based on extracted features from images. Therefore, selecting appropriate feature extraction techniques together with appropriate Machine Learning (ML) algorithms is crucial for achieving high accuracy in classification. Through the literature review, we observed a very few information about ML techniques for the classification of Gastrointestinal (GI) diseases. To solve this issue, we collected five classes of GI images from a publicly available Kvasir dataset and extracted textural features like Local Binary Pattern (LBP) and Gray Level Cooccurrence Matrix (GLCM), color features from RGB (Red Green Blue) and HSV (Hue, Saturation, and Value) color spaces and shape information from HOG. These features are combined and dimension was reduced by Principal Component Analysis (PCA). Finally, the selected features are classified with well-known ML classifier One Against All-Support Vector Machine (OAA-SVM) and obtained classification Accuracy-96.8%, Precision-96.74%, Recall-96.75% and F1-Score-96.73%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Internet of Things Based Smart Medicine Box for Monitoring Medications.

Internet of Things Based Smart Medicine Box for Monitoring M...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Manoj Kumar Shukla Sarthak Malik Yerrolla Chanti Praveen Kumar Malik Harpreet Bedi Anita Gehlot Symbiosis Institute of Technology Symbiosis International (deemed University) Pune Maharashtra India School of Computer Science and Engineering Lovely Professional University Jalandhar Punjab India School of Computer Science & Artificial Intelligence SR University Warangal School of Electronics and Electrical Engineering Lovely Professional University Jalandhar Punjab India School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India Division of Research and Innovation Uttaranchal University Dehradun Uttrakhand

Rapid technological development has made it possible for creative solutions to improve a variety of areas of our daily life. The requirement for effective drug management has grown in significance within the healthcare industry. This project describes the creation of a smart medicine box powered by the IoT, that have aims to transform the processes for dispensing and monitoring medications. The smart medicine box makes use of IoT technology to provide a complete solution for managing and ensuring medication adherence. The STM32 board, an IR sensor, a servo motor, gears, a strong box, an ESP01 wifi module, and jumper wires are the main parts of this system. Together, these elements form an intelligent and user-friendly device. The smart medicine box was developed in response to growing worries about drug mistakes, missing dosages, and a lack of reliable monitoring systems. This project addresses these problems by automating the distribution and tracking of medications by fusing the capabilities of IoT and precision engineering. Additionally, the smart medicine box connects to a companion mobile app or online interface, enabling users and caregivers to remotely monitor prescription schedules, get reminders, and access thorough medication histories. Through quick intervention and access to real-time data, this connectivity equips healthcare professionals to improve patient safety and medication adherence. An important development in medication management is the IoT-based smart medicine box, which offers patients, carers, and healthcare professionals a dependable and effective option. It might potentially improve patient outcomes, decrease prescription errors, increase adherence rates, and streamline healthcare procedures.

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