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2024 MIT Art, design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Shelke, Priya M. Pawar, Rajendra G. Pawar, Parth Rathod, Rupali Kawoor, Tanishq BRACT's Vishwakarma Institute of Information Technology Pune India Design and Technology University Engineering MIT Art Department of Computer Science Pune India

ISBN: (纸本)9798350362879

A key component of financial management portfolio optimization, which gives investors the ability to allocate capital strategically and make well-informed decisions. Traditional portfolio optimization techniques might not adequately reflect the complexities of real-world markets, despite frequently relying on mathematical models and historical data. In this paper, a novel framework for portfolio optimization with an intelligent chatbot is proposed. The research work requests information from the user, including his name, salary, investment amount, and desired investment period. Based on these parameters, it optimizes the portfolio distribution based on the user's risk appetite (low, medium, or high) and calculates the predicted rate of return. The chatbot serves as an interactive interface for investors, gathering information about their investment goals, risk tolerance, and preferences. Readers who find your article through an abstracting service may never see the rest of your article. Be sure the abstract conveys why your research problem is important and how your work moves the field forward. © 2024 IEEE.

关键词： Abstracting

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Deep Learning Approaches for Feature Extraction in Big Data Analytics 10

Deep Learning Approaches for Feature Extraction in Big Data ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dhaygude, Amol Dattatray Varma, Raj A. Yerpude, Poonam Swarnkar, Suman Kumar Kumar Jindal, Rupesh Rabbi, Fazle University of Washington Department of Data Science Seattle United States Maharashtra Pune India Indian Institute of Technology Dept of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India National Institute of Design Dept of Computer Science & Engineering Ahmedabad India Australian Computer Society Australia

ISBN: (纸本)9798350382471

In the context of big data analytics, this study examines the use of algorithms based on deep learning for feature extraction. Traditional methods usually have trouble sifting through the complexity and volume of data to find the important elements. We investigate the application of auto encoders, transformer-based models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to address this problem. Our comprehensive review of existing literature compares these deep learning techniques with traditional methods and highlights their adaptability to large-scale datasets. The efficacy and precision of these methodologies are demonstrated by empirical investigations conducted on authentic datasets across a range of disciplines, including but not limited to time-series analysis, picture identification, and natural language processing. Despite challenges like computational requirements and model interpretability, our findings indicate that deep learning-based feature extraction holds significant promise for enhancing big data analytics, leading to valuable insights and discoveries in various fields. © 2023 IEEE.

关键词： Autoencoders Big Data Analytics CNN) Deep Learning Efficiency Feature Extraction Image Recognition Natural Language Processing RNNs Scalability Time-Series Analysis Real-World Datasets Transformer Models

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Error Dependent Sampling to Reduce Transient Events in Event Based Control 3

Error Dependent Sampling to Reduce Transient Events in Event...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Romero-Perez, Julio-Ariel Sanchez-Moreno, Jose Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

One of the objectives of event-based control is the reduction of generated events to perform an appropriate process control. Among the event generation techniques used to this end, those quantifying the error signal can be found more frequently for its simplicity of implementation. However, they entail a drawback in the form of trade-off between event generation and performance. Aiming to reduce the number of events, specifically in the transient response, without degrading the performance, an error dependent sampling scheme is studied in this work. © 2024 IEEE.

关键词： Electromagnetic transients

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MC-SORT: A Motion Correction-Based Framework for Long-Term Multiple Object Tracking 27

MC-SORT: A Motion Correction-Based Framework for Long-Term M...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Xiangyu Qin, Yunchuan Li, Ruihui Tan, Guanghua Tang, Zhuo Li, Kenli College of Computer Science and Electronic Engineering Hunan University China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University China

ISBN: (纸本)9781643685489

Long-term occlusion is one of the most formidable challenges in Multi-Object Tracking (MOT). The motion models of existing SORT-based trackers are unreliable in estimating the motion states of long-term occluded targets. This is mainly because as the occlusion period increases, the increases speed of estimation errors in the motion model increases faster. In practical applications, we believe that the estimation error of the tracker during long-term occlusion is mainly concentrated in the estimation error of the motion model on the velocity of the occluded target. In this work, we have demonstrated that in the long-term occlusion period, appropriately correcting the estimated values of the motion model on the target motion velocity and fully utilizing the temporal and attribute information of the target's historical trajectory as calculation indicators of correlation are beneficial for improving the robustness of the tracker in long-term occlusion. We refer to our proposed motion correction-based framework as MC-SORT, which mainly consists of a Momentum Compensation Module (MCM) and a Backtracking Re-association (BRA) module. The former can correct the estimated value of the target's motion state during long-term occlusion, the latter uses the temporal and attribute information of the target's historical trajectory during long-term occlusion as correlation indicators to measure the degree of correlation between the target and trajectory. Our proposed MC-SORT has the characteristics of simplicity, online, real-time, and plug-and-play, particularly improving the robustness of the tracker in long-term occlusion. The extensive experimental results on the MOT17 and MOT20 datasets demonstrate the robustness and superiority of our framework. © 2024 The Authors.

关键词： Trajectories

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Predicting Financial Success: Regression Modeling of Sustainable Business Practices in Start-up Ecosystem 18

Predicting Financial Success: Regression Modeling of Sustain...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sharma, Riya Christa, Sharon Mani, Ambica Prakash Sarishma Graphic Era Deemed to Be University Department of Commerce Dehradun India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9789380544519

This research investigates the intricate relationship between sustainability adoption and the financial performance of startups in the Indian business landscape. Utilizing data from a survey conducted among 152 startups, the study employs robust statistical analyses, including regression to discern the impact of sustainable practices on financial outcomes. Positioned at the nexus of global imperatives for sustainable development and the pragmatic challenges faced by startups, the research addresses the unique context of India's entrepreneurial ecosystem. By combining empirical insights with statistical rigor, the study illuminates the mechanisms by which sustainability affects financial health. This study provides actionable information for startup decision-makers, informs policy discussions, and broadens scholarly discourse on the symbiotic relationship between sustainability and financial performance. Its findings are relevant to a wide range of stakeholders, providing a nuanced knowledge of how entrepreneurs might navigate the convergence of sustainability and economic resilience within the unique contours of the entrepreneurial scene. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： circular economy startup sustainable business model sustainable business practices triple bottom line

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A Hybrid Temporal-Magnitude Based Sampling for Event-Based Control 3

A Hybrid Temporal-Magnitude Based Sampling for Event-Based C...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Sanchez-Moreno, Jose Romero-Perez, Julio-Ariel Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been demonstrated to be suitable for control systems, reporting benefits in different categories. However, these sampling strategies also present some drawbacks, discussed in this contribution. Aiming to address some of these drawbacks, a new sampling strategy is proposed gathering characteristics of both predecessors. © 2024 IEEE.

关键词： Decentralized control

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Renewable Integration for Green Urban Mobility: A Case Study of Public Transport Depot Electrification 14

Renewable Integration for Green Urban Mobility: A Case Study...

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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Colombo, Cristian Giovanni Saldarini, Alessandro Longo, Michela Ota, Ryosuke Politecnico di Milano Department of Energy Milan Italy Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Faculty of Systems Design Japan

ISBN: (纸本)9781643685199

This paper analyses an electrification strategy for a depot for a public transport company, through an optimized model. The case study analyses both the transition of a depot able to host 10 buses towards e-buses and proposes the integration of a micro-grid, powered by PV panels to power the e-buses in a sustainable way. The study addresses key challenges such as spatial constraints, passenger demand, and financial considerations, through a Genetic Algorithm (GA). The analysis encompasses two scenarios: a mixed fleet of electric and diesel buses and an exclusive focus on e-buses. The study highlights the importance of considering both electric and diesel buses in fleet optimization, and combined with renewables emphasize the advantages of operational flexibility and sustainability. Thanks to the implementation of a micro-grid capable of providing 1123.87 kWh/day to power the e-buses as much as possible. © 2024 The Authors.

关键词： Electric buses

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DASA: An Efficient Data Aggregation Algorithm for LoRa Enabled Fog Layer in Smart Agriculture 1

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Vyas, Mayank Anand, Garv Yadav, Ram Narayan Nayak, Sanjeet Kumar Ahmedabad India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In a smart agriculture system many resource-constrained sensors are installed near the crops as well as at some strategic locations in an agriculture field to collect relevant crop and environment data in real-time. This data is then used for both critical latency-sensitive decision making as well as for long-term planning. Nowadays, with the help of smart IoT systems, resolving the problems like irrigate fields, avoid animal intrusions, notify the farmer about the seasonal rainfall etc. becomes easier. The edge of the IoT networks regularly receives a huge amount of data generated by sensors that need to be delivered to the server present in the remote data centers/cloud for additional real time control or long term decision making. However, transmitting huge amount of these IoT data across the network toward the cloud imposes a high overhead in terms of bandwidth demand and latency on the IoT network. So, the key challenge in building a smart agriculture system include high communication latency and bandwidth consumption incurred with computing over the data on the cloud. Also, frequent Internet disconnections in rural areas may lead to improper latency sensitive decision making at cloud due unavailability of data. In this paper to resolve such issues of cloud based smart agriculture system, we present a LoRa-based three-tier smart agriculture system comprised of (i) Field layer, (ii) Fog computing layer, and (iii) Cloud computing layer. In particular, a data aggregation algorithm through a LoRa enabled fog computing layer for smart agriculture (DASA) is proposed to compress the total amount of IoT data to be uploaded to the cloud. We present the performance of our proposed scheme and compare with the existing frameworks for smart agriculture system in terms of compression ratio, compression time, compression power, and amount of data transmitted to cloud from fog computing layer. Comparison results show that the proposed algorithm significantly decreases the volu

关键词： Fog computing

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Improving Breast Cancer Diagnosis through Multi-Class Segmentation using Attention UNet Model 2

Improving Breast Cancer Diagnosis through Multi-Class Segmen...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Ghantasala, G.S. Pradeep Unhelkar, Bhuvan Chakrabarti, Prasun Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9798350383867

One of the prominent causes of death for women universally is breast cancer. The number of early deaths is reduced with early detection. Using an ultrasound scan, the data examine the ultrasound images of breast cancer. Three categories, including normal, benign, and malignant images, comprise the Breast Ultrasound Dataset. Incorporating machine learning with breast ultrasound images might yield significant outcomes for the identification, classification, and segmentation of breast cancer. Through a mediocre image size of 500 500 pixels, the collection comprises 750 images. Attention U-Net/UNet is the best-known model for Multi-Class Segmentation to create an attention U-Net Model. The Model will consist of a Decoder Block Encoder Block and an Attention Gate. The attention gate layers have neurons with sizes of 256, 128, and 64, which typically correspond to the number of channels in the feature maps. The output layer in the U-Net model characteristically consists of a single convolutional layer through a kernel size of 1 1. The activation function used in the output layer was sigmoid;whichever is collective for binary classification tasks like image segmentation. The sigmoid activation function outputs values between 0 and 1, representing the possibility of each pixel fitting to the target class. The loss function stated is "binary_crossentropy," which is appropriate in binary classification tasks. The optimizer specified has Adam, which is well-liked for neural network training because of its adaptable learning rate characteristics. In contrast to conventional gradient descent techniques, the Adam optimizer adjusts the learning rate during training, which might result in faster convergence and improved results with 98% accuracy at epoch 17 with a validation loss of 0.05. ©2024 IEEE.

关键词： Image segmentation

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Investigation on distributed scheduling with lot-streaming considering setup time based on NSGA-II in a furniture intelligent manufacturing

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8697-8707页

作者： Wang, Jinxin Wu, Zhanwen Yang, Longzhi Hu, Wei Song, Chaojun Zhu, Zhaolong Guo, Xiaolei Cao, Pingxiang College of Materials Science and Engineering Nanjing Forestry University Nanjing China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom College of Furnishings and Industrial Design Nanjing Forestry University Nanjing China

Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5. © 2024 - IOS Press. All rights reserved.

关键词： Genetic algorithms

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