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Hybrid Optimization Based BERT Model for Drug detection in NLP

Hybrid Optimization Based BERT Model for Drug Detection in N...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： C G Kruthika N N Namitha Devi Vijayananda V Madlur K Asha T M Aruna Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering & Technology Mysuru India Department of Computer Science & Engineering Mangalore Institute of Technology & Engineering Moodabidri Department of Computer Science & Engineering Reva University Bengaluru India

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model’s hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-the-art models.

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The MAC Based WSN Timestamp Using Lightweight Secure detection Service

The MAC Based WSN Timestamp Using Lightweight Secure Detecti...

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

作者： Akhilesh Kalia Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In a WSN, sensors may be placed in a dangerous or unfriendly environment. Most sensors also have constrained data-transfer rates, processing power, and battery life. Therefore, it is difficult to guarantee the safety of WSN without negatively impacting the efficiency of the network. When it comes to enhancing the communication capabilities of WSN, network coding (NC) has great promise because of its potential to be both effective and efficient. However, network coding may be attacked maliciously. Safe findings have been proposed to deal with individual attacks, including data theoretic or cryptographic techniques, but they are vulnerable to attacks that combine several types of attacks. One such example is when pollution attacks and replay attacks are combined. We provide a secure detection service in this work. It monitors and manages every communications, both incoming and outgoing, and is deployed on every node in a WSN. TMAC is a lightweight timestamp-based communication verification code that is generated by the service using Exclusively OR network coding. To simultaneously prevent pollution assaults and replay attacks, a combined detection using TMAC and time synchronisation approach has been created. The detecting service's accuracy has been shown. Finally, the performance assessment demonstrates that the detection system uses less energy than existing joint detection schemes, and incurs only a marginal increase in communication capacity and computing complexity, when compared to MAC-based schemes.

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Smart Grid vs. Intelligent Grid;Artificial intelligence Takes the Power system to the Next Level

Smart Grid vs. Intelligent Grid;Artificial Intelligence Take...

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Digital Twins and Parallel intelligence (DTPI), IEEE international conference on

作者： Sahand Liasi Narges S. Ghiasi Ramtin Hadidi Holcombe Department of Electrical and Computer Engineering Clemson University Charleston SC USA

This paper discusses the concept of intelligent power systems, particularly the distinction between smart grids and intelligent grids. Smart grids represent a convergence of various energy technologies and communication systems, enabling multidomain interactions. However, in such a grid, humans are still the ultimate decision maker based on the gathered information. By adding artificial intelligence to the system, smart grids can be upgraded to intelligent grids. This article explores the cutting-edge methods and propositions of machine learning and artificial intelligence, and their influence on a plethora of smart grid applications, aiding their transition to truly intelligent grids. The piece highlights the surge in interest and rapid advancement in employing machine learning and AI techniques to effectively tackle technical obstacles across various facets of the smart grid. However, it notes unresolved issues meriting additional research, such as high-performance data processing for informed decision-making in large, intricate multi-energy systems, and lightweight solutions based on machine learning. Additionally, the article spotlights the potential of harnessing progressive computing and communication technologies such as edge computing, pervasive internet of things, and 5G wireless networks within the smart grid framework. Simulating smart grids requires overcoming challenges posed by the integration of diverse subsystems with varying time scales. Co-simulation is employed for these cases. This paper presents the state-of-the-art in simulating intelligent power systems and addresses challenges in applying co-simulation to test cases spanning various domains, including information and communication technology (ICT) and power systems. We anticipate that this study will serve as a launching pad for continued exploration and advancement in this burgeoning area of knowledge and practice.

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Research on Static Reverse Analysis technology for Security detection of Power Industrial Control Software

Research on Static Reverse Analysis Technology for Security ...

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Intelligent Power and systems (ICIPS), international conference on

作者： Jinshan Chen Sihang Yu Xuanxuan Xie Yu Cheng Zhijun Tang Chaoping Deng June Li State Grid Fujian Electric Power Research Institute Fuzhou China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

In the power industrial control software, defects of software and possible pre-implanted backdoors and malicious codes are important factors that threaten the security of the power industrial control system. The complete and accurate extraction of function call relationship is the basis of software security detection based on function call graph. Therefore, based on analyzing function call sequence of abnormal behavior of electric power industrial control software, a method of software static reverse analysis is proposed. The method identified the function call relationship optimized by the compiler to be a jmp instruction by searching the call/jmp instructions. In order to extract a simplified key function call relationship, a key function list was obtained by performing keyword fuzzy matching. Experiments show that this method can accurately and comprehensively obtain the function call relation, the simplified key function call relationship can improve the efficiency of reverse analysis and provide a better support for software defects, backdoors and malicious codes detection.

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Research on Intelligent Food Processors Supervision system for Smart City Construction

Research on Intelligent Food Processors Supervision System f...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Heng Xu Xueyu Yuan Yuyang Sun Chun He Xinya Yi School of Computer and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China School of Law Chengdu University of Technology Chengdu China

Driven by the new generation of information technology, smart city has gradually become a hot word in the new era. Smart city is to strive to achieve the deep integration of industrialization and urbanization, which improve the quality of urbanization and the quality of life of citizens, enhancing the effectiveness of urban management. In addition, with the substantial improvement of economy and science, people's pursuit of food has gradually shifted from being full to being safe and nutritious. Based on this background, food safety has become the focus of social attention. At present, there are still loopholes in China's food safety system, which fails to fully implement the technological level in the construction of food safety. The grass-roots supervision and risk prediction mechanism are not perfect enough. The State Council has issued policy documents continuously, emphasizing the full implementation of food safety, pointing out that the most important thing in market supervision at this stage is food safety. At the same time, today, with the sustainable development of smart cities, the related technologies of food safety are not yet mature. This paper proposes an intelligent food processor supervision system, which can supervise food processors by checking masks, overalls and foreign objects in hands. As mask wearing detection and overalls wearing detection have become mature, this paper uses YOLOv5 algorithm to realize real-time hand foreign object detection, realizes intelligent supervision of food processors, liberates manpower to identify and shortens inspection time and greatly improves efficiency and accuracy.

关键词： Smart cities Object detection Computer applications Inspection Real-time systems Power electronics Safety

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detection of Acoustic Scenes and Events using Audio Analysis – A Survey

Detection of Acoustic Scenes and Events using Audio Analysis...

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

作者： Dinesh Vij Yogesh Yogesh Durgesh Srivastava Hari Shankar Chitkara University Institute of Engineering & Technology Chitkara University Rajpura Punjab India

The field of Acoustic Event detection is generally overshadowed by the traditional field of Automatic Speech Recognition. But our auditory environment is made up of many sounds other than speech. Most of the current surveillance systems, diagnostic systems, etc. use video cameras and other sensors. But acoustic sensors are not used frequently because of lack of event detection accuracy in case of overlapped events. This motivates the need for developing a system that is able to identify acoustic scenes and event correctly. Researchers have worked on the acoustic events of diverse fields such as bird species recognition, classification of different type of vehicles, etc. This work has considered the all such acoustic scenes in which humans interact with the environment directly or indirectly. In this work, we have provided an overview of major studies done in this field along with the suggestions regarding what can be done in future in this field. We have also studied the various types of features related to the various acoustic scenes and events. We have summarized the recent open-source datasets and challenges conducted by IEEE AASP done in this field, which could help the future researcher to develop a foundation in this field.

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Automatic Intravenous Drip Drop Counter and Controller

Automatic Intravenous Drip Drop Counter and Controller

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Digital Futures and Transformative Technologies (ICoDT2), international conference on

作者： Rehan Tariq Abbasi Aqsa Zahoor Laiba Junaid Faisal Amin Zia Ur Rehman Sino-Pak Centre for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule Institue of Applied Sciences and Technology Dept of Biomedical Engineering Riphah International University Islamabad Pakistan

An intravenous (IV) drip is a surgical device that delivers fluids and drugs directly into a patient’s bloodstream via a vein. Although IV drips are a cost-effective, safe, and reliable tool, they also have a number of complications arise when used. The majority of intravenous infusion-related accidents are linked to the administration process' complexity. Therefore, the creation of an automated intravenous administration technique is necessary. In the proposed work the risks connected with the use of IV are examined, and a method is proposed to make IV drip monitoring and control possible based on sensing of drops passing through the drip chamber. Also in this study, the sensor-observed experimental drop rate is compared to a manually measured drop rate. Different parameters are calculated based on the automatic counting of fluid drops, with the infusion volume and flow rate being the two most crucial ones. The usage of such a device may lessen complications and give IV drip system users affluence.

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Complex Valued Markov Random Field (CMRF) Based Monte Carlo Metropolis (MCM) for Monitoring Surface Deformation

Complex Valued Markov Random Field (CMRF) Based Monte Carlo ...

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

作者： Li Meng Li Xiaoguang Yang Yang Wang Hongxia Zhao Puzhi Xinjiang Key Laboratory of Extreme Environment Operation and Detection Technology of Power Transmission & Transformation Equipment State Grid Xinjiang Electric Power Research Institute Urumqi China

The faulting of surface in earth surface deformation related with relative displacements of adj acent phases of surface. However, consideration lack of deformation state transition causes errors in prediction outcomes of surface deformation through Artificial intelligence (AI) techniques. In this research, proposed a Complex valued Markov Random Field (CMRF) based Monte Carlo Metropolis (MCM) for monitoring surface deformation. The Interferometric Synthetic Aperture Radar (InSAR) images is developed as CMRF. The corrupted phases that are represented through residues in phase information ae restored through MCM. Proposed method produced high prediction outcomes with less error rates. The performance of proposed method is evaluated with performance measures of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed method attained less MAPE of 1.82, MAE of 0.51, MSE of 0.39 and RMSE of 0.032 which is superior than other existing methods like Spatio-Temporal Association Rule Mining (STARM) and Probability Integral Method (PIM)

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Document Image Text Extraction Using Emperor Penguin Optimization with Convolutional Neural Network

Document Image Text Extraction Using Emperor Penguin Optimiz...

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

作者： Ghazi Mohamad Ramadan Bura Vijay Kumar Piyush Kumar Pareek Myasar Mundher Adnan Muhammed Basim Jabar College of MLT Ahl Al Bayt University Karbala Iraq School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq

Text attained from natural scenes different information: accordingly, it is greatly utilized in different applications to understand the scenarios of image and also to retrieve the visual information. Human beings greatly benefit from the scene image's semantic content, which helps to understand their surroundings. The process of text identification and character recognition is more difficult in these natural images since the text presents a flexible appearance in an unrestricted environment. Therefore, implemented an Emperor Penguin Optimization (EPO) with Convolutional Neural Network (CNN)-based deep learning process is employed in this framework to effectually detect the text and to recognize the character from the scene images. To eliminate noise natural scene images, a grayscale conversion and image noise removal is typically applied during the preprocessing step. The DWT, LBP, and ResNet 50 techniques are used in feature techniques process, which are assists for the classification process. Following that, extracted features are selected by EPO technique. At last, with these selected features, the character recognition and text detection are achieved successfully by CNN techniques.

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CNN-Based Location detection of Static Eccentricity in 5-X Resolver

CNN-Based Location Detection of Static Eccentricity in 5-X R...

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Electrical Machines and Drives (ICEMD), international conference on

作者： M. Emadaleslami F. Tootoonchian Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical Engineering Iran University of Technology Tehran Iran

According to the significant effects of faulty resolver condition and rotor angle error on Permanent Magnet Synchronous Motor (PMSM) drive system stability, the diagnosis of the fault occurrence and, more critically, the fault location is crucial. Among the resolver’s faults, static eccentricity has more probability and severity. So, the following study focuses on proposing a Convolutional Neural Networks (CNN)-based static eccentricity location diagnosis under stator slots. Accordingly, using Finite Element Analysis (FEA), various locations and severity of static eccentricity are generated. The Fast Fourier Transform (FFT) and Frequency Occurrence Plot (FOP) are regarded as the data preprocessing stages, applying on resolver signal voltages. Then, in the training stage, various CNNs are trained to obtain a highly accurate network. The feasibility of the suggested CNN in fault location diagnosis is shown by the confusion matrix, accuracy metric, and F1-score criterion.

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