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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh 14

A novel approach incorporating feature extraction followed b...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kundu, Debashis Rahaman, Md. Mushfiqur Sakib, Md. Nazmus Abdullah Al Sohan, Md. Faruk Fardaush Mithu, Jannatul Hossain, Afzal Aktar, Nishita American International University- Bangladesh Dept. Management Information Systems Dhaka Bangladesh Daffodil Institute of It Dept. of Computer Science and Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Dept. of Computer Science Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Savar Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation's agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database. © 2023 IEEE.

关键词： Support vector machines

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Unified Object Detection Model for Image Style Convertible Modalities

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

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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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Latency of spatial audio plugins: a comparative study

Latency of spatial audio plugins: a comparative study

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Tomasetti, Matteo Farina, Angelo Turchet, Luca University of Trento Dept. of Information Engineering and Computer Science Trento Italy University of Parma Dept. of Engineering and Architecture Parma Italy

ISBN: (纸本)9798350311044

The use of spatial audio plugins (SAPs) with Ambisonics processing and binaural rendering has become widespread in the last decade, thanks to their increased accessibility and usability. SAPs are particularly relevant in scenarios involving real-time music playing with headphones, such as networked music performance and individual recreational music-making using backing tracks. However, a crucial issue that has been largely overlooked thus far is the measurement of the processing latency introduced by currently available SAPs. Identifying which SAPs are the fastest is essential to enable designers, musicians, and researchers to create time-sensitive applications involving 3D audio. To bridge this gap, we compared nine systems formed by different SAPs that enable 3D audio management. We measured the latency of each system throughout the third-order Ambisonics plugins pipeline: encoding, room simulation, sound scene rotation, and binaural decoding. In particular, the measurements were performed utilizing different buffer sizes. Results showed that to achieve a minimization of the latency, it is necessary to use a combination of different SAPs from different systems. Based on our measurements, we propose two spatial audio systems that mix different SAPs. Considering a sampling rate of 48 kHz, a Dell Alienware x15 R2 laptop running the Windows 10 operating system, and an RME Fireface UFX sound card, the two systems achieved an overall latency of 0.33 ms and 0.94 ms respectively. © 2023 IEEE.

关键词： Audio systems

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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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An Advanced MIMO Multicarrier Framework for NTN-Based 5G Broadcast/Multicast Transmissions 11

An Advanced MIMO Multicarrier Framework for NTN-Based 5G Bro...

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11th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2024

作者： Rahman, Talha Faizur Marojevic, Vuk Sacchi, Claudio Mississippi State University Dept. of Electrical and Computer Engineering Mississippi StateMS United States University of Trento Dept. of Information Engineering and Computer Science Trento Italy The University of New Mexico Dept. of Electrical Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350385045

Satellites are teaming up with 5G, forming a non-terrestrial network (NTN), to support broadband applications over wide coverage areas. However, low latency and high reliability are the challenges in NTN which is addressed through the use of low-earth orbit (LEO) satellites. High transmission efficiency can be accomplished by employing multiple-input multiple-output (MIMO) antenna systems and multicarrier wave-forms. Conventional multicarrier techniques, such as Orthogonal Frequency Division Multiplexing (OFDM) and its variants, are sensitive to the nonlinear distortion of high power amplifiers (HPAs) and high Doppler environments. Therefore, we consider Constant Envelope OFDM (CE-OFDM) assisted with Space-Time Shift Keying (STSK) and Low-Density Parity Check (LDPC) encoding for 5G broadcast/multicast systems based on LEO satellites working in the L-band. We propose a low complexity receiver based on A Posteriori Probability (APP) STSK decoding integrated with LDPC decoding, in which we derive the APP of LDPC coded bits for the STSK-CE-OFDM transmission. We analyze the computational complexity of the proposed receiver scheme and conclude that it is effective in counteracting HPA nonlinearity and high Doppler effects inherent to LEO satellites. Numerical results show that the proposed framework outper-forms conventional multicarrier techniques with a gain as high as 16 dB in the presence of amplifier nonlinearity. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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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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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation 2

Blockchain Based Secure and Decentralized Smart Licensing of...

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2nd International Conference on information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Haque, Momenul Paul, Subrata Kumer Islam, Kamrul Della, Mursheda Nusrat Paul, Rakhi Rani Fahim, Sultan Bangladesh Army University of Engineering and Technology Dept. of Computer Science and Engineering 6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Bangladesh Army University of Engineering and Technology Dept. of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350358667

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city. © 2023 IEEE.

关键词： Smart contract

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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 ...

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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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Enhanced Image Analysis for Detecting Malaria Infection in Blood Samples 7

Enhanced Image Analysis for Detecting Malaria Infection in B...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Karthikeyan Anitha, K. Kashyap, Acchute Amrita School of Computing Dept. of Computer Science Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Engineering Dept. of Mathematics Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331527822

The methods of detecting malaria infected cells from microscopy images are discussed in detail in this paper. Malaria disease being a world health problem today, there is need to develop better diagnosing technique because of the high death rate that has been plunged to this disease. Having used a dataset of 27,558 images as infected and uninfected, this study employs Convolutional Neural Networks(CNN), Fuzzy and VGG19 models for semiautomated classification with higher speeds and accuracies as opposed to other methods. It consists of mandatory phases, which predetermine preparing the data set for training, including resizing, normalization, and data augmentation. Among to feature selection, SelectKBest method is used in order to enhance input features of the model. Initial experiments with the CNN model again revealed high accuracy greater than 90%, thereby confirming the use of the model in real-life diagnosis of malaria. In addition, the study emphasises the importance of feature extraction and the use of machine learning in the diagnosis function. The outcomes support the adoption and use of automated detection techniques as the human factor contributes greatly to errors in diagnosis, practices should embrace such technologies leading to the fight against malaria. © 2024 IEEE.

关键词： Convolutional neural networks

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Statistical and Machine Learning Perspectives on Exoplanets Prediction and Classification 1

Statistical and Machine Learning Perspectives on Exoplanets ...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Priya, M. Yagnasri Shendre, Sanjivani P. Kamble, Samrudhi S. Sridhar, Swetha Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350376517

Exoplanets are the planets orbiting stars out of our solar system. The exploration of such planets has become a growing field in astronomy but the analysis of the data related to such planets involves many challenges when traditional methods are used as this data is often vast and is obtained with the help of telescopes and space missions. This study proposes a methodology that aims to classify exoplanets, taken from datasets from the Kepler Objects of Interest (KOI) database and the NASA Exoplanet archive, as confirmed, candidate, or false positives. This approach makes use of modern analytical techniques, such as statistical analysis, probability theory, hypothesis testing, and machine learning (ML) algorithms, to facilitate the detection and classification of exoplanets. Clustering methods such as BIRCH and K-Means are also performed on the Planetary data. This study contributes to improving our knowledge and understanding of exoplanetary systems, thereby highlighting the relevance of modern analytical tools in the field of astronomy. © 2024 IEEE.

关键词： NASA

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