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Fish freshness evaluation based on MobileNetV1 and attention mechanism 22

Fish freshness evaluation based on MobileNetV1 and attention...

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proceedings of the 2022 6th international conference on Electronic Information Technology and computer Engineering

作者： Xin Guo Shenming Gu Dan Wei School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province China

ISBN: (纸本)9781450397148

Rapid acquisition of fish freshness is an important task. the traditional freshness detection of aquatic fish has the problems of damaging samples, complicated operation and long detection time. A convenient, rapid, nondestructive and accurate freshness detection method is urgently needed in production. In this study, the freshness of aquatic fish was evaluated nondestructively and rapidly by machine vision. Firstly, we select the appropriate data set, and add the attention mechanism module to the end of the MobileNetV1 with the default alpha value. Secondly, the alpha value of all models is reduced to compare the performance for subsequent deployment to the cloud and small embedded devices with limited resources. Finally, the network is trained by real data. the results show that the MobilenetV1 network with attention mechanism module can better evaluate the freshness of fish through the fisheye image. the convergence speed and accuracy of the network are improved compared with the original network, and it is more suitable for the task of determining the freshness of fish through fisheye.

关键词： image recognition attention mechanism freshness evaluation deep learning fisheye

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Apply machine-learning Model for Clustering Rowing Players 23

Apply Machine-Learning Model for Clustering Rowing Players

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proceedings of the 2023 12th international conference on Networks, Communication and Computing

作者： Patcharawit Wilaikaew Watchara Noisriphan Chien Chang Chen Jirawan Charoensuk Somchoke Ruengitinun Chalothon Chootong Department of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Sriracha Campus Thailand Department Of Computer Science and Information Engineering Faculty of Engineering Tamkang University Taiwan Department of Computer Science Faculty of Science Kasetsart University Thailand

ISBN: (纸本)9798400709265

Rowing, as a sport composed of single player or multiple players, performs body movements under certain rhythm with slight variation. the analysis of rhythm alternation or match is important on rowing research and merit our study. therefore, this study analyzes the rowing movements by the following three procedures, rowing cycle segmentation, feature extraction, rowing cycle clustering. the rowing cycle segmentation procedure segments each player's video to videos of single cycle under the analysis of MediaPipe detected joint points. the feature extraction procedure calculates features from each rowing cycle by selecting amplitudes, angles, angular speeds of 4 selected joint points. At last, the rowing cycle clustering procedure analyzes all one-cycled videos using above features by different clustering and scoring methods. three clustering methods, including K-means, Birch, and Gaussian-mixture, are experimented in this study for finding the most efficient one. A hybrid measurement from the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index, is proposed for finding the optimal clusters number. Experimental results of 15 players’ videos show that applying K-means clustering algorithm with the proposed hybrid measurement performs better for finding the rowing group.

关键词： Birch clustering Fast Fourier Transform Gaussian mixture-clustering K-means clustering Pose Detection Rowing Sports

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Predicting Lymph Node Metastasis of Colorectal Cancer in CT Scans Using Attention-Based Multiple Instance learning

Predicting Lymph Node Metastasis of Colorectal Cancer in CT ...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xie, Min Zhang, Yi Li, Xinyang Mao, Yiji Zou, Xingyu Zhang, Haixian Sichuan University Machine Intelligence Laboratory College of Computer Science Chengdu China

ISBN: (纸本)9798350337488

Accurately predicting lymph node metastasis (LNM) in colorectal cancer (CRC) contributes to develop appropriate treatment plans and evaluate postoperative prognosis. In clinical practice, preoperative diagnosis of LNM in CRC mostly relies on computed tomography (CT). Nevertheless, CT-based diagnosis of metastatic lymph nodes has a substantial misdiagnosis rate and lacks consistency among clinicians. Additionally, existing deep learning models using CT scans to predict LNM in CRC have not yielded satisfactory results due to insufficient attention given to lymph nodes. To address these issues, we propose an attention-based multiple instance learning (MIL) framework for this challenging task. Our framework incorporates a global-local cross-attention (GLCA) feature fusion module that combines global information from CT slices with local information from lymph nodes. Furthermore, an attention-based pooling approach is utilized to extract critical features and generate a bag representation. Lastly, a nested-neural memory ordinary differential equations (N-nmODE) feature enhancement module significantly augments the expressive capacity of the bag representation. A series of empirical studies show that our method achieves an overall accuracy of 74.7% and AUC of 76.8%, which represents a marked improvement over the physicians specialising in CRC and outperforms existing state-of-the-art methods. Our source code is available at https://***/SCU-MI/CAN-MIL. © 2023 IEEE.

关键词： Attention Colorectal cancer Computed tomography Lymph node metastasis Multiple instance learning

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An Empirical Analysis of Various Techniques of Solving Obstacles through Artificial Intelligence

An Empirical Analysis of Various Techniques of Solving Obsta...

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

作者： Himadri Vaidya Dharmesh Dhabliya Muhseen Jweeg Muntather Almusawi Zamen Latef Naser Amjad Hashem Ahmed Qusay Jawad Department of CSE Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand Information Technology Vishwakarma Institute of Information Technology Pune India Al-Farahidi university Baghdad Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Computer Engineer Department Alkunooze University College Basra Iraq Department of Electrical Engineering Technologies Kut University College Wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In the space sector, artificial intelligence (AI), and especially machine learning (ML), is becoming more and more popular. these days, ML algorithms are extensively used in many different contexts, including operational control of satellite constellations, autonomous navigation, and spacecraft health monitoring. there are two main types of surveys that are now available on AI applications in space missions, each with certain drawbacks. Generally speaking, the first category is out of date since it does not include more recent advancements like Deep learning (DL) and bioinspired AI algorithms. On the other hand, the second group tends to be too detailed and concentrates only on certain AI approaches, which can put off those who are looking for a more comprehensive knowledge, especially those with technical expertise in other subjects. In order to rectify these deficiencies, we provide a succinct summary that provides a thorough understanding of the uses and difficulties of AI in the space sector. Our study covers classical AI approaches used in several space mission disciplines, emphasizing their usefulness in real-world applications rather than going into formal methodologies. We also investigate bioinspired AI algorithms and point out possible uses for them. Beyond technical obstacles, we recognize and talk about significant legal ramifications of using AI in space projects. We expand our evaluation to include developments in Blockchain and Internet of things (IoT) technology in the consideration of future directions. Specifically, we highlight how AI and Blockchain are convergent and pinpoint three major application fields in the space community that stand to gain a great deal from this convergence. these areas include satellite communications, spacecraft data security, and the Internet of things in Space (IoST). Our review strives to be a useful tool for anyone interested in learning more about the complex world of artificial intelligence applications in space b

关键词： Space vehicles Surveys Satellite constellations Space missions learning (artificial intelligence) Blockchains Internet of things

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SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain Management

SUPPLYDECK – A Blockchain-Based Pharmaceutical Supply Chain...

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Advancements in Computing (ICAC), international conference on

作者： A.A.B. Amarasinghe G G.M.H. Gamagedara S Pitigala K.K H.D.A. Subawickrama E Kavinga Yapa Abeywardena Kanishka Yapa Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. the rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. there is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. this situation is worse in developing countries. the unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. this solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. this can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application.

关键词： Drugs Supply chain management Supply chains Smart contracts Manuals machine learning Companies

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ML-FEED: machine learning Framework for Efficient Exploit Detection

ML-FEED: Machine Learning Framework for Efficient Exploit De...

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IEEE international conference on Trust, Privacy and Security in Intelligent Systems and applications (TPS-ISA)

作者： Tanujay Saha Tamjid Al Rahat Najwa Aaraj Yuan Tian Niraj K. Jha Electrical and Computer Engineering Princeton University NJ USA Electrical and Computer Engineering University of California Los Angeles CA USA Cryptography Research Center Technology Innovation Institute Abu Dhabi UAE

machine learning (ML)-based methods have recently become attractive for detecting security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term memories (LSTMs) and transformers incur significant computation overheads. this overhead makes it infeasible to deploy them in real-time environments. We propose a novel ML-based exploit detection model, ML-FEED, that enables highly efficient inference without sacrificing performance. We develop a novel automated technique to extract vulnerability patterns from the Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) databases. this feature enables ML-FEED to be aware of the latest cyber weaknesses. Second, it is not based on the traditional approach of classifying sequences of application programming interface (API) calls into exploit categories. Such traditional methods that process entire sequences incur huge computational overheads. Instead, ML-FEED operates at a finer granularity and predicts the exploits triggered by every API call of the program trace. then, it uses a state table to update the states of these potential exploits and track the progress of potential exploit chains. ML-FEED also employs a feature engineering approach that uses natural language processing-based word embeddings, frequency vectors, and one-hot encoding to detect semantically-similar instruction calls. then, it updates the states of the predicted exploit categories and triggers an alarm when a vulnerability fingerprint executes. Our experiments show that ML-FEED is 72.9× and 75, 828.9× faster than state-of-the-art lightweight LSTM and transformer models, respectively. We trained and tested ML-FEED on 79 real-world exploit categories. It predicts categories of exploit in real-time with 98.2% precision, 97.4% recall, and 97.8% F1 score. these results also outperform the LSTM and transformer baselines. In addition, we evaluated ML-FEED on the attack traces of CVE vulnerability exploit

关键词： Privacy Databases Computational modeling Natural languages machine learning Transformers Feature extraction

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Explainable Sentiment Analysis application for Social Media Crisis Management in Retail 4

Explainable Sentiment Analysis Application for Social Media ...

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4th international conference on computer-Human Interaction Research and applications (CHIRA)

作者： Cirqueira, Douglas Almeida, Fernando Cakir, Gultekin Jacob, Antonio Lobato, Fabio Bezbradica, Marija Helfert, Markus Dublin City Univ Sch Comp Dublin Ireland Fed Univ Western Para Engn & Geosci Inst Santarem Brazil Univ Estadual Maranhao Technol Sci Ctr Sao Luis Maranhao Brazil Maynooth Univ Innovat Value Inst Maynooth Kildare Ireland

ISBN: (纸本)9789897584800

Sentiment Analysis techniques enable the automatic extraction of sentiment in social media data, including popular platforms as Twitter. For retailers and marketing analysts, such methods can support the understanding of customers' attitudes towards brands, especially to handle crises that cause behavioural changes in customers, including the COVID-19 pandemic. However, with the increasing adoption of black-box machine learning-based techniques, transparency becomes a need for those stakeholders to understand why a given sentiment is predicted, which is rarely explored for retailers facing social media crises. this study develops an Explainable Sentiment Analysis (XSA) application for Twitter data, and proposes research propositions focused on evaluating such application in a hypothetical crisis management scenario. Particularly, we evaluate, through discussions and a simulated user experiment, the XSA support for understanding customer's needs, as well as if marketing analysts would trust such an application for their decision-making processes. Results illustrate the XSA application can be effective in providing the most important words addressing customers sentiment out of individual tweets, as well as the potential to foster analysts' confidence in such support.

关键词： Sentiment Analysis Explainable Artificial Intelligence Digital Retail Crisis Management

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machine learning-Aided Aerial Surveillance: Enhancing Trespasser Detection Using UAVs 4th

Machine Learning-Aided Aerial Surveillance: Enhancing Trespa...

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4th international conference on Internet of things, ICIoT 2023

作者： Phang, Swee King Ma’amor, Farah Hani Azman, Afizan School of Engineering Taylor’s University Selangor Subang Jaya Malaysia School of Computer Science Taylor’s University Selangor Subang Jaya Malaysia

ISBN: (纸本)9783031856655

Since unmanned aerial vehicle (UAV) has gained popularity in the current era, UAV has been used as a form of surveillance. UAVs are known for its portable, easy to control, and unmanned features, which makes it the finest choice to install a surveillance system on. With the high rates of the property occurring in Malaysia, the idea of creating a newly improved and state-of-the-art CCTV pose as a solution to combat the stated issue. the obtained system integrates both human detection and tracking algorithm as well as face recognition system to detect trespassers caught on footage and to recognise a person’s facial attributes. the underlying motivation for the project had emerged due to the high number of cases for property crimes occurring locally and uprising surveillance applications of UAV. Upon implementing the algorithm, the obtained system must be able to fulfil the objectives, such as to critically investigate the potential of using UAV for human tracking and detection in lowering property crime cases, modify an algorithm for detecting or classifying human with an improvement of 1.14% from aerial images captured by a UAV, and apply face recognition system within the acceptable range of distance on aerial images captured. the project research would be split into 3 stages, which are code implementation for human tracking and detection, code implementation for facial recognition, and improvement of code implemented in the previous stages. During these stages, the results of the existing algorithms would be recorded on a spreadsheet software and issues emerging from the algorithm would be aimed to be resolved. In stage 1, the tested algorithm is expected to run with an improvement of 1.14%. In stage 2, testing with the algorithm built to output the acceptable distance range for the project. In stage 3, the combined algorithm is expected to work well with the footage taken in CC lab at Taylor’s Block E. the expected outcome of the proposed system is to be able to d

关键词： Unmanned aerial vehicles (UAV)

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Deep-Reinforcement-learning-Based Proportional Computation Offloading With User Equipment in Smart Home Edge Computing

Deep-Reinforcement-Learning-Based Proportional Computation O...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Haohang Xie Yunlong Tian Li Niu Miao Wang Yonghua Li School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Qingdao Haier Technology Co. Ltd National Engineering Research Center for Digital Home Network Qingdao China Qingdao Haier Intelligent Home Appliance Technology Co. Ltd Qingdao China Haier Youjia Intelligent Technology (Beijing) Co. Ltd Beijing China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

the growing adoption of Internet of things (IoT) applications has resulted in a surge in the offloading of time-sensitive and computationally intensive tasks from IoT devices to mobile edge computing (MEC) servers. In this article, we survey the task partition strategy in a smart home edge computing network with multiple user equipments (UEs) and edge servers (ESs), where each UE runs IoT application and generates computation task periodically. the generated task is allowed to be broken down into several proportions then offloaded to ESs following a task partition strategy designed to minimize both execution latency and energy consumption. We first formulate the task partition problem as a nonlinear programming problem. Subsequently, we introduce an algorithm based on multiagent deep deterministic policy gradient (MADDPG) for UEs to optimize their offloading strategies. In the proposed approach, each User Equipment leverages its local observations as input for an actor network, which generates a task partitioning strategy. Simultaneously, a critic network is trained to enhance the performance of task partitioning. through our simulations, the performance of the proposed algorithm has been confirmed to surpass that of alternative methods in terms of minimizing both delay and energy consumption.

关键词： Surveys Energy consumption Costs Multi-access edge computing Smart homes Partitioning algorithms Delays

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Depression Detection During the Covid 19 Pandemic by machine learning Techniques 4th

Depression Detection During the Covid 19 Pandemic by Machine...

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4th international conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Arora, Sofia Malik, Arun Khurana, Parul Batra, Isha Lovely Professional University Jalandhar Punjab India

ISBN: (纸本)9789811636592

Coronavirus disease 2019 is a global pandemic caused by the (SARS-CoV-2) Severe Acute Respiratory Syndrome. During the outbreak of an infectious disease, the population’s psychological reactions play a crucial role in affecting both the spread of the disease and the occurrence of emotional distress. It is recognized that there are number of techniques and tools which are used to detect the depression. To assess whether the individual is depressed or not, machine learning techniques are used. the course of action is based on texts, images, videos and emoticons etc. Among different techniques, it is analyzed that none of the technique is using the probability method to detect the depression. In probability method, one can create a database, which is a collection of words related to emotions like happiness, sadness and anger etc., the probability would be set for all such words in the database and then it will be calculated by various techniques which shows the level of depression of a person. this paper summarized the findings on the identification of depressive mood disorders, using emotion analysis methods and techniques. the author focused on research that identifies irregular activity patterns on social networks automatically. the studies selected used the classic off-the-shelf classifiers to evaluate the knowledge available for lexicon use. To resolve this problem, the web application will be developed which will perform sentiment analysis with the help of classification function which recognizes the ratio of depressive and non-depressive thoughts. © 2021, Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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