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Stock Price Forecasting with LSTM: A Brief Analysis of Mathematics Behind LSTM

Biophysical Reviews and Letters 2024年第4期19卷 361-374页

作者： Khaled A. Al-Utaibi Shehneel Siddiq Sadiq M. Sait Computer Science and Software Engineering Department University of Ha’il Ha’il Saudi Arabia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore 54000 Pakistan Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

Stock marketing prediction is really important in this digital world and for this reason, its forecasting tools are highly desired. This research examines the influence of Long Short-Term Memory (LSTM) in stock market prediction. The basic genetic algorithm behind the time series analysis tool is described with the help of a structure diagram, and its mathematical structure is explained. We also described the application of LSTM in the field of finance. LSTM is playing a vital role in prediction in several applications. Experiments on the proposed method have been conducted on stock market data. The results show that LSTM predicts the price quite fairly accurately. It outperformed other prediction models due to long-term dependency and due to its accuracy. The research demonstrates the LSTM tool’s impact on finance, particularly stock market prediction.

关键词： Cross entropy function LSTM softmax back-propagation weight and bias Mean Squared Error (MSE)

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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Sparse is enough in fine-tuning pre-trained large language models 24

Sparse is enough in fine-tuning pre-trained large language m...

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Proceedings of the 41st International Conference on Machine Learning

作者： Weixi Song Zuchao Li Lefei Zhang Hai Zhao Bo Du National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan P. R. China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan P. R. China and Hubei Luojia Laboratory Wuhan P. R. China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai P. R. China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pretrained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT.

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Application of Hyperledger Blockchain Technology to Logistics Supply Chain with IoT

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Procedia computer Science 2025年 252卷 814-823页

作者： Ahmet Sayar Muhammet Cüneyd Kurtbaş Çağlayan Sancaktar Rana Dudu Kabak Şükrü Çakmak Department of Computer Engineering Kocaeli University Kabaoglu Baki Komsuoglu Avenue No:515 Umuttepe İzmit Kocaeli 41010 Turkey Atez Software Technologies R&D Center Skyland B Blok No: 4B İstanbul Turkey

Product movement tracking stands as a pivotal aspect of logistics operations. The ever-expanding application of IoT technologies holds significant potential to enhance product tracking in logistics. However, the rapid expansion of IoT usage has also introduced several challenges, particularly concerning data privacy, security, and transparency. Blockchain technology, through distributed ledger systems, offers a secure and transparent means to manage transactions within a network. In this study, the Hyperledger Fabric blockchain network, chosen for its private and permissioned nature, leverages authentication features to ensure the confidentiality, integrity, and security of data from IoT devices. By integrating the widely adopted MQTT protocol, IoT devices involved in logistics processes communicate effectively with the blockchain network. This study presents a system where IoT devices enable the secure and automated tracking of data through blockchain’s registration and distribution mechanisms, all of which are demonstrated within a simulation environment. Developed specifically for the logistics sector, this system holds promising potential to address challenges across various fields.

关键词： Blockchain Hyperledger Fabric Supply Chain Management IoT Security Logistics Tracking Distributed Ledger Technology MQTT Protocol for IoT

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Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

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computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

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Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

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computers, Materials & Continua 2021年第5期67卷 2547-2568页

作者： Kangjik Kim Hyunbin Kim Junchul Chun Mingoo Kang Min Hong Byungseok Min Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Department of IT Contents Hanshin UniversityOsan-si18101Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan-si31538Korea R&D Center XAVIS Co.LtdSeongnam-si13202Korea

Physical contamination of food occurs when it comes into contact with foreign *** objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or ***,a preventive method that can detect and remove foreign objects in advance is *** studies have attempted to detect defective products using deep learning *** it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning *** paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning *** this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste *** on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test *** evaluated this method on images of pasta,snacks,pistachios,and red beans under the same *** results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged *** detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly *** addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

关键词： Deep-learning anomaly detection packaged food X-ray detection foreign substances detection abnormal data augmentation

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Artificial intelligence to link environmental endocrine disruptors(EEDs)with bone diseases

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第3期13卷 187-206页

作者： Khaled A.Al-Utaibi M.Idreest Ayesha Sohail Fatima Arift Alessandro Nutini Sadiq M.Sait Computer Science and Software Engineering Department University of Ha'il Ha ilSaudi Arabia Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Center for Study in Motor Science 94 via di Tiglio loc.Arancio55100LuccaItaly Center for Communications and IT Research Research Institute King Fahd University of Petroleum Minerals Dhahran 31261Saudi Anabia

Our endocrine system is not only complex,but is also enormously sensitive to the imbalances caused by the environmental stressors,extreme weather situation,and other geographical *** endocrine disruptions are associated with the bone *** is a bone disorder that occurs when bone mineral density and bone mass *** affects women and men of all races and ethnic groups,causing bone weakness and the risk of *** stresses are referred to physical,chemical,and biological factors that can impact species *** research aims to examine the impact of environmental stresses on bone diseases like osteoporosis and low bone mass(LBM)in the United States(US).For this purpose,we use an artificial neural network model to evaluate the correlation between the data.A multilayer neural network model is constructed using the Levenberg–Marquardt training algorithm,and its performance is evaluated by mean absolute error and coefficient of *** data of osteoporosis and LBM cases in the US are divided into three groups,including gender group,age group,and race/ethnicity *** group shows a positive correlation with environmental stresses and thus the endocrinology.

关键词： Endocrine disruptors osteoporosis LBM environmental stresses spatial ANNs

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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Reputation-based User Vehicle Assignment in Intelligent and Connected Vehicle Platoons

Reputation-based User Vehicle Assignment in Intelligent and ...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Subham Datta Panagiota Nikolaou Maria K. Michael KIOS Research and Innovation Center of Excellence & Department of Electrical and Computer Engineering University Of Cyprus Nicosia Cyprus KIOS Research and Innovation Center of Excellence & University of Central Lancashire Cyprus Pyla Larnaca Cyprus

Vehicle platooning is a promising and emerging framework in intelligent transportation systems. Recent works consider reputation-based approaches for head selection in single platoons, in order to optimize safety and security. When a large number of user vehicles having the same destination want to benefit from platooning, several platoons need to be formed. To this end, user vehicles are allocated to different platoons, with each platoon being led by a platoon head. To ensure necessary network bandwidth and latency, the number of user vehicles between the newly formed platoons must be balanced. However, an arbitrary assignment of user vehicles in platoons can bias the future selection of the platoon heads in such reputation-based approaches. This work considers reputation-based platooning systems and proposes an optimal approach to balance the number of user vehicles in platoons while at the same time ensures fairness in the reputation score of platoon heads. A mixed-linear integer programming formulation is proposed, which provides an optimal allocation of the user vehicles in platoons based on the above objectives. The approach is validated using multiple synthetically generated datasets using 14 different input parameters. The obtained results demonstrate the optimality of the proposed method while achieving significant time performance speedups $(\sim 140\mathrm{x}$ on average) when compared to a brute-force exhaustive method.

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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