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共 31059 条记录，以下是151-160 订阅

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Unified Object Detection Model for Image Style Convertible Modalities

Unified Object Detection Model for Image Style Convertible M...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Li, Mu-Chuan Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results. © 2023 IEEE.

关键词： Object detection

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Recognising Radicalised Reviewer Groups in Internet Product Reviews 4

Recognising Radicalised Reviewer Groups in Internet Product ...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Leena Jenifer, L. Kalaivani, S. Kalaivani, J. Sugadev, Aakash Gautham, D. Rajalakshmi Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798331515720

Online product reviews have been one of the major sources of information and consumer influence since the inception of the digital economy. On the other hand, a special challenge is made by the radical reviewer groups because they have the dual potential to mislead the audience and cause Internet harassment. Proposed method presents an extensive scheme for detecting and characterising a radical reviewer group in the context of online product reviews. In this research, network analysis is applied, coupled with sentiment analysis. In this context, trends among extremists are found through analysis, topic modelling, and natural language processing approaches. The reviewers who have strong opinions can be detected through the tone and choice of words in their assessment. Topic modelling reveals the thematic substance of reviews to detect extreme viewpoints in product conversations. Network analysis finds revised ties and affiliations. Furthermore, machine learning models They are trained for different criteria to allow reviews and reviewers to be classified into extremist and non-extremist categories. Integrating social media profiles improves the accuracy of the categorization by adding more context about the connections and opinions of reviewers. Throughout the whole study process, privacy and ethical issues are of utmost importance. To reduce false positives and guarantee the accuracy of results, human verification is used. The study also highlights how crucial it is to keep an eye on radical reviewer organizations and notify platform administrators and pertinent authorities on a regular basis in order to lessen their influence. Also, develops educational resources to raise awareness among users about the existence of extremist reviewer groups and provide guidance on critically evaluating online reviews. © 2024 IEEE.

关键词： Modeling languages

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A Study of Weed Management in Agricultural Plants using IoT and Machine Learning 6

A Study of Weed Management in Agricultural Plants using IoT ...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kakade, Subodh Pawar, Poonam Anekar, Devata Patil, Yash Rathod, Samadhan Shah, Fiyona Sinhgad Academy of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350306613

Weeds are any unwanted plant growth in a cultivated land. Weeds are vital for agriculture as not all weeds as beneficial for growth of the crop. Most weeds decrease yield while increasing the production cost. This study introduces an innovative approach to weed detection and classification in agricultural environments by harnessing the synergy of Internet of Things (IoT) technology and advanced Machine Learning (ML) algorithms. Contemporary agriculture places a significant emphasis on weed management to optimize crop yields and minimize herbicide usage. Our proposed system employs a network of IoT sensors equipped with cameras and environmental sensors to maintain continuous field surveillance. By using a network of IoT smart sensors equipped with cameras and environmental sensors, our system provides real-Time weed monitoring, and it's complemented by an IoT-controlled robot designed for automated weed cutting. Real-Time image analysis, powered by ML algorithms, particularly the YOLOv7 model, enables the prompt recognition and categorization of weeds. By providing farmers with timely alerts through an user-friendly intuitive interface, our system facilitates tailored weed management strategies. This innovative approach combines IoT technology and advanced ML algorithms for efficient weed detection and classification in agriculture. The seamless integration of these technologies offers farmers an effective method for tailored weed management, promoting sustainable and organic farming practices with minimum herbicide use. In bridging the gap between IoT and ML technologies, our research offers a promising solution for precision agriculture and underscores the significance of sustainable and organic farming methods. © 2024 IEEE.

关键词： Image Processing Machine Learning Precision Agriculture Weed Detection YOLOv7

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Metal Wall Application

A 4x4 MIMO Antenna Array for the Wi-Fi7Access Point on a Met...

引用

2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Su, Hsin-Lung Zhan, You-Sheng Dept. of Computer and Communication National Pingtung University Taiwan Dept. of Computer Science and Information Engineering National Pingtung University Taiwan

ISBN: (纸本)9798350364774

A 4×4 CPW MIMO Antenna array with an artificial magnetic conductor (AMC) reflector is presented for Wi-Fi7 access point on a metal wall application in the study. The 10-dB impedance of the integrated MIMO antenna array covers the Wi-Fi 7 operating bands, and the transmission coefficients are under -15 dB in the whole Wi-Fi7 bands. This proposed integrated MIMO antenna is low profile and has good total radiation efficiency (over 60%). The height of the integrated MIMO antenna array is 6.325 mm. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： You, Shingchern D. Liu, Chien-Hung Yen, Ching-Cheng National Taipei Universiyt of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach. © 2023 IEEE.

关键词： Investments

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RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers 1

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using ...

引用

1st Workshop on Bangla Language Processing, BLP 2023

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Dept. of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Computer Science & Engineering Dept. of Information and Communication Technology

ISBN: (纸本)9798891760585

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world’s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. © 2023 Association for Computational Linguistics.

关键词： Sentiment analysis

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A Novel Data Hiding Scheme based on Block Features Enhanced AMBTC 4

A Novel Data Hiding Scheme based on Block Features Enhanced ...

引用

4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Lin, Chia-Chen Oka, Hizrawan Dwi Zhu, Enting Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan

ISBN: (纸本)9798400708534

In this paper, we first improve Chen et al.'s ternary representation-based Absolute Moment Block Truncation Coding (AMBTC), then we combine block features and AMBTC to design our novel block feature enhanced-AMBTC-based data hiding scheme called BFI-AMBTC-based DH scheme. In our scheme, AMBTC blocks are classified into three types: complex, smooth, and flat according to the distribution of pixels in a block. For each block type, we propose different data hiding strategies according to their unique features. Experiments confirm that our hybrid data hiding strategies work well. The image quality of stego images with our hybrid data hiding strategies not only has been significantly improved but also is much closer to that offered by the conventional AMBTC compared with other existing AMBTC-based data hiding schemes. In addition, the hiding capacity achieved by our BFI-AMBTC-based DH scheme is confirmed almost two times Ou and Sun's scheme. © 2023 ACM.

关键词： Steganography

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Predictive Modeling of Environmental Parameters Using Ensemble Machine Learning Techniques 3

Predictive Modeling of Environmental Parameters Using Ensemb...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ponselvakumar, A.P. Mirthula, R.E. BarathKumar, M. Chindrella, S. Kongu Engineering College Dept. of Information Technology Erode India

ISBN: (纸本)9798331528201

Air quality monitoring plays a major role in safeguarding public health as well as environmental sanity. Present air quality monitoring approaches usually depend on apparatus and their high costs may hinder their availability and use. In this research, we propose a new machine learning based approach to estimate an air quality index (AQI). Specifically, we explore the application of random forests (RF), gradient boosting (GB) and majority voting proof (VC) for AQI estimation from data comprising several different environmental variables. We begin by preparing data for cleaning and normalization. There are specific features that define corresponding AQI predictors. Then we trained several learning machines including RF, GB and VC in order to assess the performance of AQI prediction. The model's performance is evaluated on a competition table basis. This enables hyperparameters adjustment via the competition table hence helps identify the best setup model. AQI prediction *** primary findings were that random forest models as well as gradient boosting have good predictive power while this system gains more strength when combining all three models together;vote-based one. These findings suggest that machine learning has great potential for improving air pollution control measures throughout broader sectors across the globe which will also increase its efficiency in several other goods/services areas including energy conservation programs. © 2024 IEEE.

关键词： Random forests

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Preliminary Analysis of Programming Exercise Logs to Provide Effective Feedback to Students 14

Preliminary Analysis of Programming Exercise Logs to Provide...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Tanaka, Tetsuo Shrestha, Sujan Miyazaki, Tsuyoshi Okazaki, Hidetoshi Nakakawaji, Nozomi Ueda, Mari Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan Kanagawa Institute of Technology Course of Information and Computer Sciences Graduate School of Engineering Atsugi Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information Media Atsugi Japan

ISBN: (纸本)9798350324228

The authors have developed a programming exercise system and are already using it in classes. This system provides students with a web-based programming environment, and monitor the time that students worked on exercises, logging operations such as program editing, building, execution, and testing, and their results. For teachers, it also shows how each student is progressing, the source code creation process, and the occurrence of errors. Teachers' evaluation of these functions is beneficial, but how feedback is given to students is inadequate. In this study, to examine feedback to students, we do a preliminary analysis of the operating log of this system. We used this system in a programming class of about 230 students for 15 weeks, collecting thousands to 100,000 log events such as the number of keystrokes, the number of working hours, the number of exercise programs completed, the number of copies and pastes, etc. per day. We can summarize our results as follows: 'The greater the number of keystrokes, working hours, and program completions in class, the better the results';'The number of keystrokes after the class ends (after the answer example has been published) does not affect the grades', and similar conclusions. In addition, based on these findings, we can identify conditions that increase the probability of becoming a poor performer. © 2023 IEEE.

关键词： Students

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