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Enhancing Poultry Farm Operations with IoT and Smart Manufacturing Systems

Enhancing Poultry Farm Operations with IoT and Smart Manufac...

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international conference on ICT and Knowledge engineering

作者： Wai Yie Leong Yuan Zhi Leong Wai San Leong Persiaran Perdana BBN Putra Nilai INTI International University Nilai Malaysia Schneider Electric Singapore Kallang Singapore

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

IoT and smart manufacturing can revolutionise poultry farming by increasing productivity, efficiency, and general farm management. These systems offer real-time monitoring of environmental parameters including temperature, humidity, and air quality through the integration of sensors and automation. Utilising RFID or GPS technology for livestock tracking offers valuable data about the health and behaviour of individual birds. automated feeding systems and intelligent feeders streamline the feeding procedure, guaranteeing reliable and timely chicken nutrition. IoT-enabled water quality monitoring ensures access to clean water, which is essential for the health of poultry. Predictive analytics and wearable health sensors help in early disease identification and well-informed decision-making. Operational procedures are further enhanced by supply chain optimisation, energy management, and smart lighting systems. Remote monitoring and control through mobile apps or dashboards empower farmers to oversee the farm from anywhere, while integration with ERP systems streamlines overall management, including financial tracking and resource allocation. This holistic approach to IoT and smart manufacturing in poultry farming ultimately results in improved efficiency, reduced costs, and enhanced productivity, all while prioritizing the well-being of the poultry.

关键词： Productivity Temperature sensors Automation Sensor systems Internet of Things intelligent sensors Standards Remote monitoring Smart manufacturing Farming

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Performance Evaluation of Adversarial learning for Anomaly Detection using Mixture Models 22

Performance Evaluation of Adversarial Learning for Anomaly D...

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22nd IEEE international conference on Industrial Technology (ICIT)

作者： Pawar, Yogesh Amayri, Manar Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada Grenoble Inst Technol G SCOP Lab Grenoble France

ISBN: (纸本)9781728157306

With the rapid advancement in wireless technology and with the introduction of next-generation 5G-wireless networks, a huge amount of data is generated and transmitted across computer networks worldwide. As a result, we have seen a continuous increase in new network threats and anomalies that creates a significant research challenge to handle the vulnerability along with data integrity, confidentiality, and reliability. More precisely, wireless networks are considered to be highly vulnerable to advanced persistent threat (APT) actors. In this paper, we evaluate an adversarial mechanism for anomaly detection using different statistical mixture models. Indeed, to prevent attacks like data poisoning and evasion from affecting the performance of anomaly detection systems, adversarial learning has proven to be very effective in many research studies. The performance of the mechanism, in which several recently proposed mixture models are integrated, was evaluated using both NSL-KDD and UNSW-NB15 data sets, in terms of accuracy, Detection Rates (DRs), False Positive Rates (FPRs), and computational time.

关键词： Mixture models adversarial learning variational learning anomaly detection

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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45th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Gu, Rong-Fei Zhao, Li-Ming Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong Univ Ctr Brain Like Comp & Machine Intelligence Dept Comp Sci & Engn Key LabShanghai Educ Commiss Intelligent Interac 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ RuiJin Hosp Clin Neurosci Ctr RuiJin Mihoyo LabSch Med 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

ISBN: (纸本)9798350324471

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

关键词： Classification (of information)

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Solving Non-linear Optimization Problem in engineering by Model-Informed Generative Adversarial Network (MI-GAN) 22

Solving Non-linear Optimization Problem in Engineering by Mo...

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22nd IEEE international conference on data Mining (ICDM)

作者： Li, Yuxuan Zhao, Chaoyue Liu, Chenang Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA Univ Washington Dept Ind & Syst Engn Seattle WA USA

ISBN: (纸本)9798350346091

Optimization models have been widely used in many engineering systems to solve the problems related to system operation and management. For instance, in power systems, the optimal power flow (OPF) problem, which is a critical component of power system operations, can be formulated using optimization models. Specifically, the alternating current OPF (AC-OPF) problems are challenging since some of the constraints are non-linear and non-convex. Moreover, due to the high variability that the power system may have, the coefficients of the optimization model may change, increasing the difficulty of solving the OPF problem. Although the conventional optimization tools and deep learning approaches have been investigated, the feasibility and optimality of the solutions may still be unsatisfactory. Hence, in this paper, based on the recently developed model-informed generative adversarial network (MI-GAN) framework, a tailored version for solving the non-linear AC-OPF problem under uncertainties is proposed. The contributions of this work can be summarized into two main aspects: (1) To ensure the feasibility and improve the optimality of the generated solutions, two important layers, namely, the feasibility filter layer and optimality-filter layer, are considered and designed;and (2) An efficient model-informed selector is designed and integrated to the GAN architecture, by incorporating these two new layers to inform the generator. Experiments on the IEEE test systems demonstrate the efficacy and potential of the proposed method for solving non-linear AC-OPF problems.

关键词： generative adversarial network (GAN) modelinformed generation optimal power flow (OPF) non-linear optimization

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Hybrid Cat-Artificial Fish Swarm Based Node Deployment Optimization in intelligent Transportation IoT 19

Hybrid Cat-Artificial Fish Swarm Based Node Deployment Optim...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yi, Lingzhi Luo, Bin Zhu, Chenlu Deng, Xianjun Xia, Yunzhi Wu, Hengshan Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430073 China University of South China School of Civil Engineering Hengyang421001 China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Hubei Chutian Expressway Digital Technology Co. Ltd Wuhan430074 China

ISBN: (纸本)9798350346558

The integration of Internet of Things (IoT)-based highway bridge structural health monitoring (HBSHM) and intelligent transportation systems (ITS) plays an essential role in detecting damage for large and complex bridge structures. The HBSHM provides ITS with bridge health condition information for real-time traffic management. The ITS provides HBSHM with the required traffic data to improve the HBSHM system's accuracy and reliability. Node deployment optimization (ndO) is critical in HBSHM as sensor node deployment directly affects the quality of collected data. To better solve the ndO problem in HBSHM, a novel hybrid cat-artificial fish swarm algorithm (CAT-AFSA) based on the Artificial Fish Swarm Algorithm (AFSA) and Cat Swarm optimization (CSO) is proposed in this paper. The algorithm introduces seeking and tracing behaviors of CSO into AFSA, which effectively expands the search space of artificial fish and reduces the blind search of it. The performance of the CAT-AFSA is first tested using four benchmark functions, and then its validity and feasibility for the ndO are verified using an engineering example. The simulations demonstrate that the CAT-AFSA has better search performance and computational efficiency compared with the AFSA and CSO. The CAT-AFSA has a broad application prospect in the field of ndO in civil engineering. © 2022 IEEE.

关键词： intelligent systems

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Adversarial Variational Modality Reconstruction and Regularization for Zero-Day Malware Variants Similarity Detection 22

Adversarial Variational Modality Reconstruction and Regulari...

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22nd IEEE international conference on data Mining (ICDM)

作者： Molloy, Christopher Banks, Jeremy Ding, Steven H. H. Charland, Philippe Walenstein, Andrew Li, Litao Queens Univ Sch Comp Kingston ON Canada Def R&D Canada Valcartier Miss Crit Cyber Secur Sect Quebec City PQ Canada BlackBerry Ltd Waterloo ON Canada

ISBN: (纸本)9781665450997

Matching malware variants in the same malware family has always been a significant challenge for Cyber Threat Intelligence (CTI). For zero-day malware that does not belong to an existing family, a timely matching of its variants is essential for effective threat tracing and prompt response to the cyber incident. However, malware variants are of diverse forms that make them difficult to match. Additionally, the information extracted from a given malware sample is inaccurate, especially on zero-day malware. Existing malware solutions only focus on detecting known malware or find if two samples are similar without creating any reusable representation of the samples. In this paper, we propose the first practical and efficient solution for zero-day malware variant matching with reconstruction. By combining multi-modality learning and a Siamese-based structure, our model can navigate across different modalities and match zero-day variants. To address the missing or noisy modality issue, we propose a Conditional Variable Autoencoder with a Generative Adversarial Network for heightened resolution. We trained the model on 100,000 malware triplet pairs. Our experiments on real-world noisy samples show that the model out-performs the state-of-the-art and can accurately match not only zero-day malware, but also out-of-sample benign binaries of the same category.

关键词： Cyber-security Deep learning Mining heterogeneous data

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AutoML Technologies for the Identification of Sparse Models 22nd

AutoML Technologies for the Identification of Sparse Models

引用

22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Liuliakov, Aleksei Hammer, Barbara Machine Learning Group Bielefeld University Bielefeld Germany

ISBN: (纸本)9783030916077

automated machine learning (AutoML) technologies constitute promising tools to automatically infer model architecture, meta-parameters or processing pipelines for specific machine learning tasks given suitable training data. At present, the main objective of such technologies typically relies on the accuracy of the resulting model. Additional objectives such as sparsity can be integrated by pre-processing steps or according penalty terms in the objective function. Yet, sparsity and model accuracy are often contradictory goals, and optimum solutions form a Pareto front. Thereby, it is not guaranteed that solutions at different positions of the Pareto front share the same architectural choices, hence current AutoML technologies might yield sub-optimal results. In this contribution, we propose a novel method, based on the AutoML method TPOT, which enables an automated optimization of ML pipelines with sparse input features along the whole Pareto front. We demonstrate that, indeed, different architectures are found at different points of the Pareto front for benchmark examples from the domain of systems security. © 2021, Springer Nature Switzerland AG.

关键词： Pipelines

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Investigating the Use of Automation in Cryptocurrency Trading

Investigating the Use of Automation in Cryptocurrency Tradin...

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Disruptive Technologies (ICDT), international conference on

作者： Inderpreet Kaur Yashica Chauhan Utsav Gupta Sagar Malik Department of Computer Science and Engineering Galgotias College of Engineering and Technology (AKTU) Uttar Pradesh India Department of Computer Science and Engineering Galgotia College of Engineering and Technology (AKTU) Uttar Pradesh India

CryptoBot is a groundbreaking automated cryptocurrency trading system that answers the issues faced by traders in an environment where market dynamics change swiftly. Hence, CryptoBot employs a holistic approach of data gathering, preprocessing, predictive modelling, and real-time decision-making, thus it keeps analyzing real-time market data. The system then uses state of the art machine learning algorithms to make accurate predictions about price movements and determine when to enter and exit trades. This program also shows very promising results indicating a significant improvement in trading performance compared to traditional human-based strategies. CryptoBot therefore becomes an instrument of innovation for both experienced and non-experienced cryptocurrency investors. Thus, with its flexibility, precision, and smart automation users become more competitive reshaping how the investor interacts with the volatile but lucrative world of cryptocurrencies. As digital assets continue to reshape finance, CryptoBot stands at the forefront, exemplifying the future of intelligent trading.

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Improving of 3-D structure estimation methods by image using ambient light 22

Improving of 3-D structure estimation methods by image using...

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22nd international conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Miwa, Naoya Sasaki, Tohru Yamabe, Eri Nagahata, Yusuke Mir, Bilal Ahmed Terabayashi, Kenji Graduate School of Science and Engineering for Education University of Toyama Japan Department of Mechanical and Intellectual Systems Engineering University of Toyama Japan

ISBN: (纸本)9781998999118

Ambient light is a concept proposed by visual affordance theory that refers to the distribution of light a single viewpoint receives from its surroundings. By observing the changes in the luminance distribution of light produced by the structure of the environment from a single viewpoint, we can estimate the surrounding structure. When we apply this natural mechanism of recognizing the outside world to image measurement technology, it is possible to obtain the information necessary to recognize the surrounding environment by observing the ambient light without necessarily detecting or recognizing the object. For example, this methods can be used to estimate the 3-D structure in an image using only simple algorithms, without the need for complex image processing or prior data for machine learning. In a previous study, we proposed a structure estimation method to understand the structure of the surrounding environment by capturing ambient light as luminance, thus providing a viewpoint in an indoor environment. The layout of the surface of the surrounding environment structure is estimated by assuming that a horizontal wall is a vertical wall and a vertical wall is a horizontal wall, and that the indoor structure is composed of vertical walls, horizontal walls, and space. However, this method sometimes failed to estimate the structure correctly because changes in the position of the lighting in the room affected the estimation results. In addition, it sometimes misrecognized the boundary of the surface when estimating the structure. In contrast, by using the dynamic thresholding method proposed in the current study, we were able to obtain results that were robust to changes in illumination position. In addition, by focusing on the presence or absence of luminance continuity in an image, we were able to accurately detect the boundary of the surface. In this paper, we describe these results of improving the 3-D structure estimation method. © European Society for Precisio

关键词： Luminance

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High precision detection of small hepatocellular carcinoma using improved EfficientNet with Self-Attention 22

High precision detection of small hepatocellular carcinoma u...

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22nd IEEE/ACIS international conference on Computer and Information Science, ICIS 2022

作者： Wang, Yuxiang Huang, Zhongming Tiangong University School of Electronic and Information Engineering Tianjin China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Small hepatocellular carcinoma (SHCC) is among the most fatal cancers, and spotting SHCC symptoms in the early stage is vital for conducting timely treatments. Thus, auxiliary detection algorithms have been developed, especially after convolutional neural networks (CNN) made great progress in processing medical images. However, their performance is confined by dataset, resulting in limitations to accurately detect SHCC appearing small and diffusive in CT images. In our work, Self-Attention mechanism has been introduced as the front end and EfficientNet as our backbone network, contributing to a novel SHCC detection algorithm able to automatically spot subtle lesions through our image-wise annotated dataset in a weakly supervised manner. In our model, the Self-Attention module extracts ROI and background features from original CT images and generates weighed feature map to the EfficientNet. In our backbone network, the EfficientNet learns from input feature maps and is weakly supervised under image-wise annotations. With the pre-process of Self-Attention, our data size for EfficientNet has been reduced, thus enhancing learning efficiency and reducing time consumption. After training on over 1.5k CT images, our model has achieved decent detection performance comparing to other state-of-the-art methods while remaining acceptable complexity. © 2022 IEEE.

关键词： Convolutional neural networks

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