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Optimizing and Validating Performance of 40 Gbps Optical System 1

International Conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Gupta, Shradha Choudhary, Shilpa Babulal, Kanojia Sindhuben Sharma, Sanjeev Department of Applied Science and Humanities G.L. Bajaj Institute of Technology and Management Greater Noida India Department of Electronics and Communication Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Technology Central University of Jharkhand Ranchi India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

The purpose of this paper is to optimize the performance of the RZ modulated 40 Gbps optical system by using RSM optimizer for pre- and post-compensation of dispersion. There are many input factors whose variation causes improvement of optical signals such as laser power, RZ duty cycle and EDFA gain (pre-and post-compensation), which were optimized to determine their effects on the signal characteristics. The output signal quality was characterized using noise power and Q-value. Using OPTSIM simulator a 40 Gbps optical network design and run over the 31 combinations as defined by RSM algorithm. These provide the optimized parameters. These optimized input parameters are again validated by running the optical system on these values. The pre-position of DCF shows the best performance with a percentage error of less than 5% for Q-value and 0.3% for noise power. This compensation technique is applied for RZ modulated system and validate by Q-value and noise power with percentage error. The optimized results matched with the simulated result and thus validate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical systems

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Study of Express Package Data Processing under Specific Scenario 10

Study of Express Package Data Processing under Specific Scen...

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10th IEEE International Conference on computer science and Network technology, ICCSNT 2022

作者： Duan, Xiaodong Wu, Cuiling Ning, Tao Institute of Computer Science Dalian Minzu University State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

Semi-Automatic machine vision-based sorting in package sorting relies on specific sensors to read form information and synchronize it to the control system to complete a sort. To reduce hardware costs, express package detection and form recognition in a single camera are investigated. To improve the picking accuracy, a multi-category express package dataset is constructed for a specific picking scenario. In order to reduce the effects of diverse package categories and uneven brightness of the picking environment, an exponential mean brightness equalization algorithm and an method are proposed to improve the mAP value of the detection model from 94% to 97.2%. In order to improve the accuracy of form recognition, the images are rotated by multiple angles to complete the correction, and then the images are cropped for interferents, which improves the accuracy from 79% to 90%. © 2022 IEEE.

关键词： Sorting

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Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

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Research on Traffic Sign Recognition in Night Environment based on Deep Learning 10

Research on Traffic Sign Recognition in Night Environment ba...

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10th IEEE International Conference on computer science and Network technology, ICCSNT 2022

作者： Liang, Ranran Ning, Tao Jia, Xiangkun Institute of Computer Science Dalian Minzu University State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

In order to decrease in the accuracy of traffic sign recognition due to dim light at night, a novel YOLOv5 algorithm is proposed in this paper, which adds the Improved Adaptive Histogram Equalization (IAHE) method to image pre-processing, adjusts image brightness and contrast to highlight the related information about traffic signs. In view of the higher requirements of driving assistance system on the processing speed of recognition model and the standard convolution mode of backbone network is improved to the depth separable convolution method for model lightweight processing. To address the problem of wrong and missed detection generated by prediction box suppression, the conventional NMS is replaced by Weighted Boxes Fusion WBF to generate prediction box. Experiments have demonstrated that the improved algorithm has improved detection accuracy and decreased processing time for a single image compared with the original YOLOv5 algorithm in the self-built night environment dataset. © 2022 IEEE.

关键词： Convolution

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Detrended fluctuation analysis of sentiment patterns in literary texts

Detrended fluctuation analysis of sentiment patterns in lite...

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37th ECMS International Conference on Modelling and Simulation, ECMS 2023

作者： Gaża, Lukasz Drożdż, Stanislaw Oświęcimka, Pawel Stanuszek, Marek Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków31-155 Poland Institute of Nuclear Physics Polish Academy of Sciences Kraków31-342 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland

ISBN: (纸本)9783937436807

Temporal correlations of the sentiment content in consecutive sentences are studied based on a large corpus of the world-famous literary texts in four major European languages (English, French, German, and Spanish). For quantifying the related characteristics in terms of the Hurst exponents the Detrended Fluctuation Analysis (DFA) is employed. The results obtained provide a clear indication for the existence of persistent correlations as revealed by the Hurst exponents significantly larger than 1/2 in all the four languages studied. An interesting result that requires a deeper study is the identified fact that in many texts the DFA indicates two different regimes of scaling and thus two different Hurst exponents. In those cases - quite universally - the cross-over occurs at the scale corresponding to about 200 sentences and the Hurst exponents at the scales above this value are even larger which indicates the presence of stronger long-range temporal correlations than those at the shorter scales. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.

关键词： Sentiment analysis

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Hybrid Deep Learning Approaches for Data Security in Cloud Environments 2

Hybrid Deep Learning Approaches for Data Security in Cloud E...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rekha, S. Sharma, Manish Kannan, Sathish Upadhye, Vijay Jagdish Patil, Harshal Rajkumar, S. PSG College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore641 014 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat Waghodia India Computer Science and Engineering Maharashtra Pune412115 India Institute of Technology Hawassa University Faculty of Manufacturing Department of Mechanical Engineering Ethiopia

ISBN: (纸本)9798350327533

More recent studies have concentrated on methods to improve the security and privacy of cloud operations for the benefit of user data. For example, in order to safeguard sensitive data kept by a cloud-deployed procedure, it is important to guarantee secure transmission of sensitive data across multiple stakeholders. Researchers have investigated deeply into the subject of cloud security concerns, which include data breaches, denial of service, data loss, service rejection, and threats from malicious users. These issues typically stem from problems like multi-tenancy, lack of control over data, and trust issues. Cloud providers are primarily responsible for ensuring the security of the cloud platform. This study proposes a CNN-BiLSTM-AM hybrid model that combines Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention Mechanism (AM). In order to determine how well our proposed hybrid model worked, this study tested it extensively against the traditional CNN and Long Short-Term Memory (LSTM) techniques. An established standard for evaluating intrusion detection systems, the CICIDS 2017 dataset was used in the study. The experimental results demonstrated that the proposed hybrid model outperformed the competition. The proposed hybrid model delivers the highest performance measure of accuracy (99.2%), detection rate (98.807%), and precision (99.59%). © 2024 IEEE.

关键词： Cardiovascular Disease Deep Learning Electrocardiogram Heartbeat Normalization

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Data-Driven Feature Analysis for Student Performance Prediction

Data-Driven Feature Analysis for Student Performance Predict...

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Innovative Engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Maha Said Al-Anqoudi Mireilla Bikanga Ada Stephen McQuistin College of Computing and Information Sciences University of Technology and Applied Science Nizwa Oman School of Computer Science University of Glasgow Glasgow UK School of Computer Science University of St Andrews St Andrews UK

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

We have witnessed the widespread adoption of online teaching and learning platforms in recent years. Teachers employ a variety of learning activities and techniques to follow their students’ learning progress, including summative assessments. However, concerns have been raised about the trustworthiness of summative assessments, such as quizzes and tests. At the same time, online teachers have found other learning activities that are not typically included in predictive machine learning models of online platforms are effective in helping them identify student learning status. This study uses two feature selection techniques, filter and embedded, along with a teacher self-reported questionnaire to evaluate which learning activities influence students’ performance in summative assessments like quizzes. Our findings reveal that features from non-summative learning activities can effectively predict students’ performance in an online environment. Teachers agree on the importance of the identified learning activities’ features in helping them determine their students’ learning status. The study provides practical recommendations for educators, course designers, and policy-makers to optimize online assessment strategies.

关键词： Discussion forums Distance learning Education Machine learning Predictive models Feature extraction

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Post-Covid-19 Pandemic IT Project Management Skills and Challenges 2

Post-Covid-19 Pandemic IT Project Management Skills and Chal...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ali, Liaqat Taleb, Nasser Ali, Atif Abu-Alsondos, Ibrahim A. Naseem, Hina Yousaf, Farhan Abdelhakim, Mohamed University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Cyberjaye63100 Malaysia College of Computer Information Technology Dubai503000 United Arab Emirates Allama Iqbal Open Universioty Islamabad Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Management and Technology Pakistan

ISBN: (纸本)9798350335644

Since December 2019, the COVID-19 pandemic has harmed social, financial, and work life. The novel coronavirus has caused problems in all business sectors, including Information technology(IT). Many Projects have been stopped or delayed due to the impact of this pandemic. Most of the companies recognized the importance of IT to achieve competitive advantage and to survive. Companies are investing a lot of money in IT projects. More than 60% of IT project fail. Lack of professional IT project management is one of the main reasons for that fail. Managing IT projects is a complex problem. Crises such as COVID 19 and uncertainty are increasing the complexity and challenges in IT projects management. This paper investigates what are the IT project management Key success factors required during and after the Pandemic. Secondary data were collected from literature review in the field of IT project management. an online questionnaire was used to collect the primary data from 107 IT firms. 323 respondents were participated. It is concluded that the following Key success factors are required: e-communication, centralization of data, online project monitoring, designing new policies and guidelines for incorporating new work culture, gaining and establishing project risk management exercises, especially cybersecurity and data protection by gaining access to the latest tools, establishing a culture for strict usage of Personal Protective Equipment (PPEs) to help project managers complete projects safely, and top management support. © 2023 IEEE.

关键词： COVID-19

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Utilizing Artificial Intelligence for Plant Phenotyping in Soilless Farming: An Innovative Deep Learning Approach on a Unique Dataset 1

Utilizing Artificial Intelligence for Plant Phenotyping in S...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Reddy, D. Jaya Narayana Krishna Priya, R. Kumar, J. Arun Prasad, P. Dharani Nawaz, Mustafa Venu, Nookala G Pullaiah College of Engineering and Technology India University of Technology and Applied Sciences Research and Consultancy Department Governorate of Musandam Oman Vel Tech Rangarajan Sagunthala R&d Institute of Science and Technology Department of Ece India Dept of Pharmacology Tirupati India Sri Sairam Institute of Technology Department of Computer Science and Engineering India Centre for IoT Madhya Pradesh Gwalior474005 India

ISBN: (纸本)9798350377002

In the dynamic landscape of agriculture, the convergence of technological advancements and sustainable practices has become imperative. The emergence of soilless farming, driven by hydroponics and aeroponics, presents a promising solution to address food security challenges while mitigating environmental concerns. Central to the success of soilless farming is efficient plant phenotyping, which traditionally relies on labour-intensive methodologies. However, this paradigm is shifting with the integration of Artificial Intelligence (AI) and Deep Learning techniques. This research endeavours to pioneer an innovative approach to plant phenotyping in soilless farming by harnessing the power of AI. Leveraging a unique dataset meticulously curated from controlled hydroponic and aeroponic environments, our study aims to redefine the boundaries of agricultural research and practice. By employing state-of-the-art Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we dissect complex phenotypic traits encompassing leaf morphology, biomass accumulation, and physiological responses. Through iterative model refinement and validation, we strive to develop a robust framework capable of real-time phenotypic assessment across diverse plant species and growth stages. By synthesizing diverse environmental conditions and perturbations, we augment the original dataset, enhancing model generalization and adaptability. Moreover, through transfer learning techniques, Furthermore, the scalability and accessibility of AI technologies pave the way for democratizing agricultural innovation, fostering inclusive growth and resilience in the face of global challenges. © 2024 IEEE.

关键词： Hydroponics

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Smart Spaces: Occupancy Detection using Adaptive Back-Propagation Neural Network 2

Smart Spaces: Occupancy Detection using Adaptive Back-Propag...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Saleem, Muhammad Khan, Muhammad Saeed Issa, Ghassan F. Khadim, Assma Asif, Muhammad Akram, Ali Sheraz Nair, Harish K G School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Skyline University College School of Information Technology Sharjah United Arab Emirates Skyline University College School of IT University City Sharjah Sharjah1797 United Arab Emirates

ISBN: (纸本)9798350335644

In recent years, the concept of smart spaces and occupancy detection is crucial for creating more efficient and sustainable buildings that prioritize both energy efficiency and occupant comfort. By leveraging advanced technologies and real-time data analysis, building managers can optimize energy consumption, reduce operating costs, and improve the overall user experience of the space. One important aspect of this optimization process is the detection of occupancy in smart buildings, which can help to control various systems like Heating, Ventilation, Air Conditioning (HVAC) and lighting. Accurate occupancy detection is a key factor in reducing energy consumption and cost optimization for building control systems, particularly in the areas of HVAC and lighting. To this end, Adaptive Back-Propagation Neural Network (ABPNN) algorithms is applied to a dataset generated by CO2, humidity, temperature, and light sensors to enhance the accuracy of occupancy detection in smart *** this study, the ABPNN based Bayesian Regularization (BR) algorithm is found to have the highest precision rate, with 70% (5701 samples) of the dataset used for training, 15% (1221 samples) for validation, and 15% (1221 samples) for testing. Furthermore, the BR algorithm demonstrated an impressive accuracy rate of 99.8% when compared to other algorithms. Smart spaces with advanced sensors and BR algorithms, such as the BR algorithm, accurately detects occupancy and optimize systems like HVAC and lighting to improve energy efficiency and occupant comfort. This reduces energy consumption and operating costs, while providing insights for optimizing space usage and improving overall building performance. © 2023 IEEE.

关键词： Carbon dioxide

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