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A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

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Annals of Data science 2023年第1期10卷 183-208页

作者： Kurani, Akshit Doshi, Pavan Vakharia, Aarya Shah, Manan Department of Computer Engineering Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering Indus University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Petroleum University Gujarat Gandhinagar382007 India

From exchanging budgetary instruments to tracking individual spending plans to detail a business's profit, money-related organisations utilise computational innovation day by day. Here in this paper, we focus on the significance of innovation in accounts such as financial risk management and stock prediction. We discuss two significant algorithms that have a notable role in stock forecasting. Artificial Neural Networks (ANN), as absenteeism of some data points, does not hamper the network functioning. Secondly, Support Vector Machines (SVM) has several features, and due to simple decision boundaries, it avoids over-fitting. The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios. The paper covers the recent past studies to explore the concepts and methodologies through which ANN's and SVM's have been used. Additionally, the paper incorporates significant aspects of novel methods and technologies in which ANN as a hybrid model like ANN-MLP, GARCH-MLP, a combination of the Backpropagation algorithm and Multilayer Feed-forward network, yields better results. Simultaneously, SVM's have been successfully applied in stock prediction, giving an accuracy of about 60%–70% for simple SVM, which is further improved by combining methods like Random Forest, Genetic Algorithm more accurate outcomes. Further, we present our thoughts on where SVM's and ANN's stand as prediction algorithms and challenges like the time constraint, current scenarios, data limitation, and cold start problems were raised. Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can further be enhanced with their integration with other novel techniques resulting in hybrid methodologies. It will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting. © 20

关键词： ANN Machine learning SVM

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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

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science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust multi-domain descriptive text classification leveraging conventional and hybrid deep learning models

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International Journal of Information technology (Singapore) 2024年第5期16卷 3219-3231页

作者： Bhowmik, Shovan Sultana, Sharmin Sajid, Ahmed Arian Reno, Saha Manjrekar, Aakash Department of Computer Science and Engineering Bangladesh Army International University of Science and Technology Syedpur Bangladesh Department of Computer and Information Sciences University of Delaware Newark United States Department of Computer Science University of Massachusetts Lowell Lowell United States Vidyalankar Institute of Technology University of Mumbai Mumbai India

Since an unprecedented amount of online information in the form of unstructured texts is generated daily, researchers have started to focus on the development of robust mechanisms with the help of Natural Language Processing (NLP) and Artificial Intelligence (AI) to predict sentiments. Reliable sentiment classification models can not only avoid the spreading of misinformation but also help people to sieve genuine data by creating fact-checking tools on the user end. The main focus of this work is to investigate the performance of six conventional Deep Learning (DL) models followed by one hybrid DL model in three domains (fake news identification, spam filtering, and cyberbullying detection) of opinion mining tasks by employing efficient deep architectures capable of predicting sentiments even if long sentences are provided. Our empirical results show the rigid performance of all models on a corpus with maximum accuracy of 99.55%, 97.57%, and 93.00% for fake news, spam email, and cyberbullying domains accomplished by Convolutional Neural Network (CNN), Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) hybrid, and CNN models accordingly. Our work also exhibits a comparison of compilation times for every model to speed up the training time in this state-of-the-art. Contrary to the previous studies, we have taken long descriptive paragraphs for training purposes with a maximum length of 3336 words in a single sentence. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Neural Network Convolutional Neural Network Cyberbullying Fake News Sentiment Analysis

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Plant Disease Detection using Image Processing with Machine Learning 4

Plant Disease Detection using Image Processing with Machine ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kiran, Ajmeera Lokesh Naik, S.K. Raj, M Silpa Palvadi, Srinivas Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Hyderabad India India Koneru Lakshmaiah Education Foundation Department of Computer Science & Information Technology India

ISBN: (纸本)9798350300093

Crop diseases pose a severe challenge to food security, yet in many parts of the world it is still challenging to promptly diagnose them owing to a lack of necessary infrastructure. Image processing may be the most effective method to anticipate this issue and provide findings that are remarkably precise. Support vector machines and deep CNN were used in this research by improving training effectiveness and accuracy. In this study, Plant Disease Dataset is used which involves images of healthy and diseased plants collected by under some controlled conditions, SVM (support vector machine which used for feature extraction of images) and CNN (it will classify the dataset images into diseased plant or healthy plant) and further it'll be recommending the fertilizers according to the plant diseases. © 2023 IEEE.

关键词： Support vector machines

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Enhancing Student Support with AI: A College Assistance Chatbot Using NLP and ANN 2

Enhancing Student Support with AI: A College Assistance Chat...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Abdul Razak, M.S. Manoj Kumar, M.V. Nirmala, C.R. Naseer, R. Prashanth, B.S. Sneha, H.R. Bapuji Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Davanagere577004 India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560064 India

ISBN: (纸本)9798350372892

The rapid adoption of chatbots in various domains has revolutionized user support systems, particularly in educational institutions. This paper presents the development and implementation of a College Assistance Chatbot designed to aid university students in navigating academic and administrative queries. Leveraging Natural Language Processing (NLP) techniques and deep learning models, specifically an Artificial Neural Network (ANN), the chatbot efficiently understands and processes student inquiries. The system's architecture is meticulously crafted to manage communication effectively, ensuring timely and accurate responses. An extensive dataset was collected from college resources and pre-processed to train the neural network. The chatbot's performance was rigorously evaluated through a series of experimental campaigns, achieving an accuracy of 80.07% after 1000 epochs. The results indicate that the chatbot can reliably handle a wide range of student queries, providing a scalable solution for enhancing student support services. This innovative approach offers significant improvements over traditional methods, demonstrating high potential for broader application in educational settings. © 2024 IEEE.

关键词： Students

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Adaptive finite-time command-filtered backstepping sliding mode control for stabilization of a disturbed rotary-inverted-pendulum with experimental validation

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JVC/Journal of Vibration and Control 2023年第5-6期29卷 1431-1446页

作者： Mofid, Omid Alattas, Khalid A Mobayen, Saleh Vu, Mai The Bouteraa, Yassine Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia School of Intelligent Mechatronics Engineering Sejong University Seoul Korea Republic of Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Sfax Sfax3038 Tunisia

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method. © The Author(s) 2022.

关键词： Inverted pendulum

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Security on mobile cloud computing using cipher text policy and attribute based encryption scheme

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Materials Today: Proceedings 2023年 80卷 3059-3063页

作者： Swetha, Madireddy Latha, M. Department Of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Vels Institute of Science Technology & Advance Studies Tamil Nadu India

Mobile Cloud Computing (MCC) is a new paradigm that has been emerged by the advances in the Cloud Computing for Mobile devices to access Cloud services. The data security challenges against the data thefting, deleting, corrupting, or exploiting are existed as the data storage and access to/from the cloud has been most popular. In order to handle these data security issues in the cloud and to provide protection for the data, some innovative encryption methods have been developed. One of such encryption methods is Attribute-Based Encryption (ABE) that has a more control on the data in cloud as which is accessed by whom. Since from the recent years, many of academic researchers have paying a widespread of attention on using advanced cryptographic techniques to securely store, process, also share data over the untrusted cloud environment. One of the promising cryptographic techniques which can solve the open challenge of regulating the fine-grained access control of important data over the distributed cloud is the Cipher text Policy – Attribute Based Encryption (CP-ABE). Therefore, a survey of work is presented in this paper on different CP-ABE methods for the secure mobile cloud computing architecture. This paper presents some works that are focused on secure data storing, accessing and sharing using CP-ABE methods over the untrusted cloud environments. All the works that are studied are analyzed and compared with each other in the results to find the best out of it. © 2021

关键词： Mobile cloud computing

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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Advanced Schedulability Testing for Adaptive Mixed-Criticality Systems under Weakly Hard Real-Time Constraints 5

Advanced Schedulability Testing for Adaptive Mixed-Criticali...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Rabbiun, Hossein Mohammadi, Jamal Shirazi, Mahmoud Kargahi, Mehdi Department of Computer Science and Information Technology Zanjan Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran

ISBN: (纸本)9798331529284

This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time Systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skipped when the system operates in the high-criticality (HI) mode. The current AMC-WH schedulability test, based on the skip-over model, in which low tasks are guaranteed to meet m-s out of m consecutive deadlines, where s is the number of allowed skips and m is the cycle length, only calculates the worst-case response times (WCRT) for low tasks under a fixed execution pattern post-mode switch. To address this limitation, we propose a new sufficient schedulability test for AMC-WH using the (m,k)-firm model, which allows various execution patterns for low tasks after a mode switch, enhancing the flexibility and applicability. Additionally, we develop an exact schedulability test for AMC-WH based on response time analysis (RTA). This test modifies the existing exact tests for the AMC algorithm, incorporating updates to state-space exploration and pruning rules to account for low task execution after a mode switch. Simulation results validate the effectiveness of our tests, demonstrating enhanced schedulability analysis and optimized resource allocation. © 2024 IEEE.

关键词： Space research

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