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Plant Disease Detection: A Comparative Study of Deep Learning Approaches

Plant Disease Detection: A Comparative Study of Deep Learnin...

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International conference on Inventive Research in computing Applications (ICIRCA)

作者： K T M Princy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Demand for food is increasing every year. It causes insecurity in food items. Farmers are using large amounts of fertilizers and pesticides to meet their needs. Fertilizers are substances applied to plants to increase their productivity. According to the survey, India ranks second in fertilizer use globally and first in south asian countries. Pesticides are used to kill and repel certain kinds of pests. the overuse of pesticides and fertilizer causes a lot of problems. Most pesticides are harmful and have a terrible effect on the environment and human health. Sulphur, Malathion, Mutchler, Copper Oxychloride, Prorate, and Mancozebs have commonly used pesticides. Overuse of pesticides causes severe health issues like Cancer and that leads to death. Managing and controlling pesticides is very important to ensure safe public health. there are numerous reasons for the over usage of pesticides. the Main reason is crop diseases and pest attacks. Inefficient ways to determine plant disease and late diagnosis cause more use of pesticides. Determining the correct plant disease and types is essential to overcome this threat. this work deals with an effective way to discover plant disease detection using deep learning models. Numerous learning methods are present for the early detection and classification of diseases. In this work, we apply different deep learning algorithms such as CNN, ResNet, Inception model, Vgg16 and Vgg19. We get an accuracy in the range of 72% to 92%.

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Human Activity Recognition Based on Wavelet-CNN Architecture 5

Human Activity Recognition Based on Wavelet-CNN Architecture

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5th asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Zhang, Dongwen Zhang, Lin Yi, Qingwu Huang, Lu Zhang, Guanghua Hebei University of Science and Technology School of Information Science and Engineering Hebei050018 China Hebei University of Science and Technology School of Information Science and Engineering State Key Laboratory of Satellite Navigation System and Equipment Technology 54th Research Institute of China Electronics Technology Group Corporation Hebei050018 China Hebei University of Science and Technology School of Information Science and Engineering 54th Research Institute of China Electronics Technology Group Corporation Hebei050018 China

ISBN: (纸本)9781665426305

Human activity recognition (HAR) has become an important research field in pervasive computing and has attracted extensive attention from researchers at home and abroad. However, traditional recognition methods rely heavily on artificial feature extraction, which greatly affects the generalization ability of the model. therefore, this paper designs a deep learning model based on wavelet transform and convolution neural networks. Firstly, the waveform data of multi-channel sensor is decomposed into low-frequency and high-frequency components by wavelet transform (WT) after window sliding segmentation, which are combined as the input data of network model. then, convolution neural networks with different convolution kernels are used to extract multidimensional features efficiently, and the max-pooling layers are used to filter the interference noise caused by human unconscious jitter. Finally, through the output classification of full connection layer, the accurate recognition of human activity state is realized. In order to verify the effectiveness of the designed model, we evaluate the performance of the model from the convergence speed, loss and accuracy of the model, and compare it with the more advanced recognition model on the public dataset of OPPORTUNITY. Finally, the proposed architecture Wavelet-CNN achieves 91.65% F-measure and has higher activity recognition ability. © 2021 IEEE.

关键词： Wavelet transforms

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Octahedron-shaped Convolution for Refining Aorta Semantic Segmentation 5

Octahedron-shaped Convolution for Refining Aorta Semantic Se...

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5th asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Xiang, Xi Luo, Gongning Zhao, Pengfei Wang, Wei Wang, Kuanquan Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781665426305

Refining 3D aorta segmentation is essential for clinical aorta analysis. the small tubular diameter of the aorta branches and the discontinuity of neighboring information make it difficult to get a continuous semantic segmentation map. In this paper, we propose a novel octahedron-shaped convolution (OSC) based on VNet and signed distance map (SDM). OSC aimes to aggregate more contextual information for each sample point in the aortic branches with small tubular diameters. SDM is introduced as auxiliary information to measure the similarity of neighboring points, and it provides weights for feature fusion. Furthermore, we embed the learned 3D offset field into OSC to avoid inaccurate segmentation on the region around the narrow tubular structures. the OSC module prolongs the predicted length of small aorta branches and then improves the tubular continuity of the aorta segmentation map. We evaluate the OSC module on our-collected dataset and MICCAI ASOCA2020 coronary artery dataset. Our method has achieved the state-of-the-art results in terms of Dice and Jaccard metrics. © 2021 IEEE.

关键词： Convolution

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Multifactorial Bare Bones Sine Cosine algorithm with Chaotic-based elite learning for Multi-tasking Optimization 5

Multifactorial Bare Bones Sine Cosine algorithm with Chaotic...

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5th asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Ning Wang, Lei Jiang, Qiaoyong Li, Xiaoyu Wang, Bin Zhang, Guangnan Xi'an University of Technology Computer Science and Engineering Xi'an China School of Computer Science Baoji University of Arts and Sciences Baoji China Key Laboratory of Industrial Automation of Shaanxi Provice Shaanxi University of Technolog Hanzhong China

ISBN: (纸本)9781665426305

the problem of multi-task optimization is a new research topic in the field of evolutionary computing in recent years, and it has attracted more and more attention from the academic community. Compared with single-objective optimization and multi-objective optimization, multi-task optimization can use the similarity and complementarity between tasks to solve different optimization tasks at the same time. However, as the population evolves to the later stage, the ability of the two tasks to learn from each other gradually declines. In order to solve this problem and enhance the effectiveness of knowledge transfer between different tasks, this paper combines the bare bone sine cosine algorithm (BBSCA) and the elite learning strategy based on chaos mapping (ELM) into MFEA, and proposes the MFBBSCA-ELM algorithm. Since BBSCA and ELM have different search neighborhoods and are highly complementary to the analog binary crossover used in the classic MFEA algorithm, this article combines BBSCA and ELM, which is also the motivation of this article. In addition, the integration of BBSCA and ELM can reduce the probability of MFEA falling into a local optimum. Finally, this article verifies the effectiveness of integrating BBSCA and ELM into MFEA on the classic MTO benchmark problem. the experimental results show that compared with the classic MFEA, the performance of the MFBBSCA-ELM proposed in this paper has been significantly improved. © 2021 IEEE.

关键词： Evolutionary algorithms

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Hindi-English Code Mixed Hate Speech Detection using Character Level Embeddings 5

Hindi-English Code Mixed Hate Speech Detection using Charact...

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5th International conference on computing Methodologies and Communication, ICCMC 2021

作者： Rahul Gupta, Vasu Sehra, Vibhu Vardhan, Yashaswi Raj Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665403603

Hinglish is a portmanteau word for 'Hindi' and 'English', and refers to the informal "language"predominantly used in the South-asian (Indian) Sub-Continent, a blend of the two languages it derives its name from. It considerably differs from the English language in grammar, syntax, punctuations, phonetics and accent, as well as in *** it is more convenient to use English for certain technical words, sports events, scientific phenomena, and other things, mixed usage of English and regional languages has gained considerable prominence in day-to-day conversations and Social Media. this research aims to create an independent and self-sufficing model that classifies Hingish texts as Hate Speech, Abusive or *** prevalent use of code-mixed language in the subcontinent, the sensitive nature of hate speeches, and the need of a self-sufficient model for Hinglish, together serve as the motivation for this *** have used character level embeddings for Hinglish Language which has the potential to most efficiently extract the context from Hinglish sentences given the level of variation in syntax and semantics of the code-mixed (a language that is a combination of two or more languages) language. Later we trained various deep learning classifier models. Hybridisation of GRU with Attention Model performed best among more than 12 models experimented with. the use of Character Level Embeddings, GRU, and attention layer are novel to Hate Speech Detection in Hinglish Code-Mixed Language. © 2021 IEEE.

关键词： Embeddings

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Temporal-Aware Self-Supervised Learning for Unsupervised Video Anomaly Detection 5

Temporal-Aware Self-Supervised Learning for Unsupervised Vid...

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5th asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Shang, Guoqian Huang, Chao Su, Jingyong Xu, Yong Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781665426305

Video anomaly detection (VAD) is commonly formulated as the discrimination of events that do not confirm to the regular patterns in videos. Recently, deep neural network-based VAD approaches have gained remarkable progresses. Existing unsupervised approaches usually achieve VAD by frame reconstruction or prediction, and then identifying anomalies according to the reconstruction or prediction errors. However, these approaches suffer from two limitations: (1) they cannot obtain the semantic features of normal training samples. (2) It is suboptimal because of the non-alignment between the proxy and actual tasks. To address the above issues, we present a novel temporal-aware self-supervised learning framework to obtain the high-level semantic features and to perform VAD by solving multiple pretext tasks. In particular, we utilize temporal transformations to form multiple pretext tasks (transformations prediction) for VAD. A 3D encoder is trained to obtain semantic features by jointly solving these pretext tasks. then, multi task heads utilize these features to solve different pretext tasks. In the inference phase, multiple task losses are used for calculating the final anomaly score. Extensive experiments are conducted on two benchmarks, which shows that the proposed method outperforms state-of-the-arts. © 2021 IEEE.

关键词： Deep neural networks

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A Human-Robot Interactive Mahjong Playing System Based on Visual Recognition Using a Convolutional Neural Network 21

A Human-Robot Interactive Mahjong Playing System Based on Vi...

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5th International conference on Computer science and Application Engineering, CSAE 2021

作者： Wu, Zhifang Han, Jiuqiang Liu, Erhu Lyu, Hongqiang Faculty of Electronic and Information Engineering School of Automation Science and Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Guangdong Artificial Intelligence and Digital Economy Laboratory Guangdong Guangzhou510335 China

ISBN: (纸本)9781450389853

Mahjong is a popular tabletop game in China, Japan, and other asian countries. As an incomplete information game, it is more complicated than other complete information games, such as Go, chess, and shogi. With the rapid development of service robot, there have been several human-robot interactive playing systems for complete information games so far, but mahjong is not the case. In this paper, a human-robot interactive mahjong playing system (HRMPS) was developed. HRMPS consists of five modules, including central host, robot players, visual kit, mahjong conveyor, and interactive software. the central host serves the communication between four robot players, which grab a tile on mahjong conveyor and recognize its face via visual kit, and make an action decision by themselves or by human opponents with the help of the interactive software. In HRMPS, to visually recognize a total of 27 different mahjong faces in uncontrolled conditions, a deep convolutional neural network was adopted to achieve an accuracy of 99.71% with a running time of 29ms. the experimental results tell that HRMPS is applicable in human-robot interactive mahjong game.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Human robot interaction

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Anthropometric Considerations for Occupational Well-Being of Women Weavers Engaged in Commercial Handloom Weaving

Anthropometric Considerations for Occupational Well-Being of...

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Joint conference of the asian Council on Ergonomics and Design and Southeast asian Network of Ergonomics Societies, ACED SEANES 2020

作者： Bori, Geetashree Bhattacharyya, Nandita Kalita, Mira Department of Family Resource Management and Consumer Science College of Community Science Assam Agricultural University Jorhat Assam India

ISBN: (纸本)9783030633349

Handloom sector of India has served the economy in terms of employment where 31.45 Lakhs household are engaged in handloom weaving and women weavers comprises of 71.8% of the total workforce (Handloom census 2019–20). the occupational risk of women weavers is a key factor in handloom weaving. It is possible to reduce by developing a well-equipped ergonomically designed workplace considering the anthropometric measurements. the present study was undertaken to assess the anthropometric considerations for the occupational wellbeing of women weavers engaged in commercial handloom weaving. Various parameters were used i.e., weight, height, grip strength which were recorded by weighting balance, anthropometric rods, grip dynamometer. A multistage sampling design was used. A total of 90 weavers, thirty weavers engaged in each category of looms was selected purposively. the anthropometric dimension of weavers working in Frame-loom using jacquard, Frame-loom using dobby, and throw-shuttle loom using dobby was taken and compared with different female and male anthropometric data. the mean stature of the women weavers was found to be 153 cm ± 3.39 and high variation between the ranges of 5th and 95th percentile values. the result shows that the anthropometric dimension of women weaver varies with the male population. the proportion of stature indicated for dimension as sitting height, buttock popliteal length, and sitting popliteal height was found to be 0.501, 0.280, and 0.268. therefore, an implementation of these data will help to create comfortability, safety, the well-being of the user and enhance the productivity of commercial handloom weavers. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anthropometry

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Minimal-envy conference Paper Assignment: Formulation and a Fast Iterative Algorithm

Minimal-envy Conference Paper Assignment: Formulation and a ...

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Artificial Intelligence Technology (ACAIT), asian conference on

作者： Mao Tan Zhuocen Dai Yuling Ren Toby Walsh Martin Aleksandrov Hunan National Center for Applied Mathematics Xiangtan University Xiangtan China School of Computer Science Xiangtan University Xiangtan China School of Computer Science and Engineering University of New South Wales Sydney Australia Institute of Software Engineering and Theoretical Computer Science Technical University Berlin Berlin Germany

ISBN: (纸本)9781665426312

In conference review management, it is very common that many papers being assigned are not liked by some reviewers due to their specialties or personal interests. In this paper, we consider a reviewer might only envy other reviewers who get a better set of papers than his. In this context, we proposes a new bi-objective optimization assignment model to efficiently and fairly assign papers to many competing reviewers, in which an index that measures the amount of envy is introduced. A fast iterative algorithm is proposed to solve the problem. the experimental results on real datasets shows that the algorithm achieves efficient and fair results and good computing performance, especially on large scale problems. this work is also meaningful for a number of similar allocation or matching problems.

关键词： Computational modeling conference management Dispatching Complexity theory Iterative methods Indexes Resource management

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Human Activity Recognition Based on Wavelet-CNN Architecture

Human Activity Recognition Based on Wavelet-CNN Architecture

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Artificial Intelligence Technology (ACAIT), asian conference on

作者： Dongwen Zhang Lin Zhang Qingwu Yi Lu Huang Guanghua Zhang School of Information Science and Engineering Hebei University of Science and Technology Hebei China State Key Laboratory of Satellite Navigation System and Equipment Technology The 54th Research Institute of China Electronics Technology Group Corporation Hebei China

ISBN: (纸本)9781665426312

关键词： Wavelet transforms Pervasive computing Analytical models Time-frequency analysis Convolution Computational modeling Neural networks

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