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Novel design of a sentiment based stock market index forecasting system

Soft Computing 2024年第20期28卷 12081-12095页

作者： Roy, Partha Depatment of Computer Science and Engineering Bhilai Institute of Technology Durg India

This article proposes a novel idea for creating a sentiment-based stock market index forecasting model by amalgamating price and sentiment data hidden in the price pattern itself. The state-of-the-art methodologies used in forecasting stock markets involve gathering sentiment data from external sources like tweets, but the proposed model is unique in the sense it extracts the sentiment information from the price itself, making it more reliable and easier to test and implement. In the proposed system the simple daily time series is converted to an information enriched fuzzy linguistic time series with a unique approach of incorporating information about the sentiment behind the Open High Low Close (OHLC) price formation that took place at every instance of the time series. A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system it into a forecasting system. A number of experiments were conducted using the proposed model on Nifty-50 index values (5 years) and it was found that the Root Mean Squared Error (RMSE) value came around 1.03 and RMSE% came around 1.72% which is quite small compared to number of observations and hence this gives a strong indication that the proposed system has the capability to perform good quality of forecasts. The model is simple and easy to implement with very nominal memory requirements, compared to other type of models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Marketplaces

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Novel Capsule Network Algorithm Based Image Classification with Efficiency

Novel Capsule Network Algorithm Based Image Classification w...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Adhitya Kannan, S.G. Uma Devi, S. IFET College of Engineering Depatment of Computer Science and Engineering Tamil Nadu Villupuram India

ISBN: (纸本)9798331540685

Despite the significant impact of convolutional neural networks (CNNs) across various domains, one of their limitations is the lack of consideration for spatial orientations. Capsule networks have the potential to address these issues by incorporating the storage and routing of pose information for the features they extract throughout their architectures. This involves seeking agreement between the lower-level feature extraction and the higher-level poses at each layer. Through the storage and routing of posture information across their architectures, capsule networks aim to resolve these issues. The fundamental components of the trainable segmentation engine include an encoder network, a corresponding decoder network, and a pixel-wise classification layer. The quantitative evaluations conducted demonstrate that Capsule Networks exhibit robust performance, competitive inference times, and superior memory efficiency compared to alternative architectures. The training and testing process utilizes datasets such as MNIST and CIAFR-10 datasets. The obtained results indicate that the proposed approach successfully outperforms CNNs in addressing image classification problems. © 2024 IEEE.

关键词： Image segmentation

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A Review on the Capability and Smart Contract Potential of Block chain Technology 3

A Review on the Capability and Smart Contract Potential of B...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Singh, Devansh Malhotra, Mrs Vimmi Depatment of Computer Science and Engineering Dronachary College of Engineering Gurgaon India

ISBN: (纸本)9781665464871

There has been a lot of buzz about blockchain technology lately, thanks to the success of digital currencies like Bitcoin and Ethereum. All of these applications of blockchain technology have received a lot of attention. Bitcoin and Ethereum were able to process 7 and 15 TPS, respectively, when they were launched, whereas VISA and PayPal were able to process 1700 and 193 TPS. Scalability, which may be described as the capability to modify the block size to accommodate the rising traffic, is the most significant barrier to the widespread adoption of blockchain technology. This article aims to examine blockchain applications, including their applications, smart contracts, and the solutions to those problems. It is vital to scale blockchains while maintaining their fundamentally decentralised nature to appreciate these technologies' promise fully. The successful implementation of scalability solutions is necessary for several reasons, including the provision of services whose performance is on par with that of prevalent technologies, the support of innovative applications, and the maintenance of the workload produced by an expanding user base. In the future, this study will be extended by considering the options that contributors provide as an initiative towards resolving these issues. © 2023 IEEE.

关键词： Smart contract

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Optimization Models for Teaching Load Formation of University Teachers 1

Optimization Models for Teaching Load Formation of Universit...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chigambayeva, Diana Goryakin, Makar Batova, Polina Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers’ workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution. ©2024 IEEE.

关键词： functional model mathematical model study load educational process educational work types of educational work

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Concat_CNN: A Model to Detect COVID-19 from Chest X-ray Images with Deep Learning

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SN computer science 2022年第4期3卷 305页

作者： Saha, Priyanka Neogy, Sarmistha Depatment of Computer Science and Engineering Jadavpur University Kolkata 700032 India

COVID-19 is creating havoc on the lives of human beings all around the world. It continues to affect the normal lives of people. As number of cases are high, a cost effective and fast system is required to detect COVID-19 at appropriate time to provide the necessary healthcare. Chest X-rays have emerged as an easiest way to detect COVID-19 in no time as RT-PCR takes time to detect the infection. In this paper we propose a concatenation-based CNN model that will detect COVID-19 from chest X-rays. We have developed a multiclass classification problem which can detect and classify a chest X-ray image as either COVID + ve, or viral pneumonia, or normal. We have used chest X-rays collected from different open sources. To maintain class balancing, we took 500 images of COVID, 500 normal images, and 500 pneumonia images. We divided our dataset in training, validation, and test set in 70:10:20 ratio respectively. We used four CNNs as feature extractors from the images and concatenated their feature maps to get better efficiency of the network. After training our model for 5 folds, we have obtained around 96.31% accuracy, 95.8% precision, 92.99% recall, and 98.02% AUC. We have compared our work with state-of-the-art pretrained transfer learning algorithms and other state-of-the-art CNN models referred in different research papers. The proposed model (Concat_CNN) exhibits better accuracy than the state-of-the-art models. We hope our proposed model will help to classify chest X-rays effectively and help medical professionals with their treatment. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC Chest X-ray Classification Convolution neural network COVID-19 Deep learning Image processing Sensitivity Specificity

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Intelligent Movie Recommender based on Emotions 3

Intelligent Movie Recommender based on Emotions

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Naik, Archana Ushashree, P. Bharadwaj, Isha Bhatnagar, Archita Potula, Divya Fouziya Nitte Meenskshi Institue of Technology Department of Computer Science and Engnieering Bangalore India Geethanjali College of Engineering and Technology Depatment of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350343632

Recommender systems are designed to help users navigate through a vast space of possible options and find the best ones that meet their needs and preferences. User state of mind and emotions are crucial for making an appropriate decision for personalization of products like music, news, and movies. In these cases, user emotions can strongly influence their overall experience and satisfaction, often more so than objective factors like plot, genre, or ratings. Recognizing the importance of user emotions, modern recommender systems have begun incorporating emotional analysis techniques into their algorithms. These techniques can help the system better understand and capture the emotional states of users, which in turn allows for more accurate and personalized recommendations. © 2023 IEEE.

关键词： Artificial neural network Convolution neural network Live Imaging Web scraping

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Precision Agriculture: Enhancing Potato Disease Classification using Deep Learning 15

Precision Agriculture: Enhancing Potato Disease Classificati...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Negi, Arvind Singh Rawat, Anchal Mehra, Pramod Graphic Era Hill University School of Agriculture Dehradun India Graphic Era Deemed to be University Depatment of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350370249

Potato, a highly cultivated and sought-after crop globally, ranks among the top crops alongside rice and wheat. Widely embraced as an occupation in over 125 countries, potato farming faces challenges, particularly infections and diseases. Typically, these problems fall into one of two categories: Both early and late blights have the potential to seriously damage crops and lower yield production. This research underscores the potential of Deep Learning (DL) methods in the categorization of potato diseases, providing valuable insights for the development of effective and automated disease management strategies. We employed a Convolutional Neural Network (CNN) to conduct a classification of potato leaf diseases. This research study encompasses the preprocessing of leaf image data, the training of a CNN model on this data, and an evaluation of the model's performance on a test set. Our tests indicate the CNN model excels in identifying potato leaf diseases-Early Blight (EB), Late Blight (LB), and Healthy conditions-with an impressive 97% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Wideband Tunable Transmission Type Polarization Converter Metasurface using Graphene for Terahertz Frequency Regime 4

Wideband Tunable Transmission Type Polarization Converter Me...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mistri, Hiranmay Ghosh, Anumoy Sardar, Abdur Rahaman Ramkrishna Mahato Government Engineering College Department of Electronics and Communication Engineering Purulia India National Institute of Technology Mizoram Department of Electronics and Communication Engineering Aizwal India Ramkrishna Mahato Government Engineering College Depatment of Computer Science and Engineering Purulia India

ISBN: (纸本)9798350352931

This article represents a broadband transmission type linear-to-circular polarization converter (LTCPC) realized using a graphene metasurface for the terahertz (THz) frequency regime. The composition of the unit cell includes a lossy-SiO2 substrate with a graphene-based sub-wavelength mono-layer structure on the top and a similar but very thin gold sub-wavelength structure at the back side to cover it partially. This metasurface can be used to transform waves with linear polarization into circularly polarized waves from 9.15 THz to 13.0 THz, i.e., about 3.85 THz bandwidth and about 35 percent of fractional bandwidth (FBW). The chemical potential (CP) and relaxation time (RT) can be varied to tune the graphene metasurface over a vast range of frequencies. All physical measurements of the unit cell geometry are optimized for performance optimization. The proposed lumped parameter circuit for the unit cell is also incorporated. The proposed unit cell can perform invariably up to 40° incident angle. © 2024 IEEE.

关键词： Terahertz waves

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Performance Evaluation of Genetic Algorithm and Flower Pollination Algorithm for Scheduling Tasks in Cloud Computing 9th

Performance Evaluation of Genetic Algorithm and Flower Polli...

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9th International Conference on Signal Processing and Integrated Networks, SPIN 2022

作者： Singh, Pardeep Bathla, Gourav Panwar, Deepak Aggarwal, Alankrita Gaba, Shivani Computer Science & Engineering Graphic Era Hill University Dehradun248001 India School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun248001 India Computer Science & Engineering Manipal University Jaipur Jaipur India Depatment of Computer Science PIET Haryana Panipat India Department of AML Chandigarh University Mohali India

ISBN: (纸本)9789819913114

Resource management during the cloud computing process is one of the prominent issues currently under research. The reason behind this is to procure an optimal utilization of these resources with the least cost and time. Allocation and scheduling of users’ tasks over the servers is an NP-hard problem, which can be solved by applying various heuristic and meta-heuristic approaches. With this motive, the paper has analyzed the performance of two meta-heuristic approaches, i.e., Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) in scheduling the tasks over virtual machines (VM). Tasks to VMs allotment is mainly responsible for the utilization of virtual resources which are actually running over physical machines (PM) or servers. The prime parameter considered in the research is makespan which indirectly measures the consumption of resources for performing a set of tasks. Simulation for the research is performed for a defined set of tasks that need to be allocated over a particular number of servers using the cloudsim tool. Results are measured and analyzed for both the algorithms applied in the scheduling process and also a default approach of first come first serve (FCFS) is compared with the overall results. Analyses prove the GA approach overperforms the FPA in reducing the makespan in various configurations considered. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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