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Gold Price Prediction using ARIMA model 2

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Nanthiya, D. Gopal, S.B. Balakumar, S. Harisankar, M. Midhun, S.P. Kongu Engineering College Department of Computer Science and Engineering Erode Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai India Kongu Engineering College Department of Computer Technology Erode Perundurai India

ISBN: (纸本)9798350347982

The most valuable metal in the world is gold. It is the most sought-after commodity available. The study and forecasting of the daily gold price rate and the future gold price rate using machine learning algorithms and methodologies. Due to the multifactorial and nonlinear structure of the gold market, it is impossible to anticipate the gold price, which is influenced by a variety of external variables like marketing conditions, economic crises, oil price rate hikes, tax benefits, and interest rates. Using machine learning to enhance the performance of certain types of activities. It is used to the forecasting of financial variables, with an emphasis on equities rather than commodities. It focuses on how predictions are made utilising datasets and statistical analysis. Using ensemble-based machine learning methods such as Linear Regression, ARIMA Model, Random Forest Regression. The predictions are derived from a dataset of gold price rates. The performance measurements are MAE and RMSE. In Linear Regression values are 19.82,24.41. ARIMAA model 0.040, 0.046 and Random Forest is 0.150, 0.156. The results suggest ARIMAA model predict value in high accuracy. © 2023 IEEE.

关键词： Multiple linear regression

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Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

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computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid Muhammad Rizwan Abdulatif Alabdulatif Abdullah Alnajim Meznah Alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

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Dynamic Framework for Optimized Cloud Service Selection Using Adaptive Weighting and Enhanced TOPSIS 5

Dynamic Framework for Optimized Cloud Service Selection Usin...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Swathi, V.N.V.L.S. Nakka, Vanaja Farhana, Shaik Archana, M. Reddy, K. Deepthi Vathsala, A. Vani Department of Computer Science and Engineering CVR College of Engineering Rangareddy Ibrahimpatnam India Telangana Social Welfare Residential Degree College for Women Suryapet India

ISBN: (纸本)9798350361155

As cloud services become increasingly integral to business and technology landscapes, effective selection and composition of these services are paramount. This paper introduces a novel framework that significantly enhances the cloud service selection process by incorporating an advanced Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The proposed framework uniquely adapts to real-time user preferences and market trends through a dynamic weighting system, thereby refining decision-making accuracy. Comparative scenario-based testing shows an 88% improvement in decision accuracy over existing methods. Additionally, the modified TOPSIS algorithm achieves execution time reductions between 12% and 45%, outperforming standard benchmarks. This framework is further distinguished by a dynamic weighting mechanism tailored to user feedback, which has resulted in high user satisfaction, averaging 4.2 out of 5. Comprehensive evaluation criteria within the framework ensure a thorough assessment of cloud service suitability, establishing a robust, adaptive, and user-centric approach to navigating the complex cloud service landscape. © 2024 IEEE.

关键词： Decision making

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Pothole Detection based on Machine Learning and Deep Learning Models 3

Pothole Detection based on Machine Learning and Deep Learnin...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Tripathi, Mukesh Kumar Hasini, Sagi Homamalini, Madupally Neelakantappa, M. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9798350348347

Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formation of potholes and cracks on the roads, which can cause damage to vehicles or pose a physical danger to occupants, particularly in underdeveloped countries. Identifying potholes in real-time can help drivers avoid them and prevent accidents. Furthermore, recording their locations and sharing them can assist other drivers and road maintenance organizations take prompt corrective measures. In our attempt to address the issue of pothole detection, The paper aims to combine the latest technological advancements. We aim to develop practical, reliable, adaptable, and modular solutions. To achieve this, we will compare the performance of Random Forest, a machine learning model, with CNN, a deep learning model, in detecting potholes. The experiments were conducted on both models using multiple datasets, and a conclusion was drawn to bring out the benefits of the model with 99% accuracy using CNN and 95% accuracy using Random Forest. © 2023 IEEE.

关键词： Deterioration

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Revolutionizing Assessment: AI-Powered Evaluation with RAG and LLM Technologies 2

Revolutionizing Assessment: AI-Powered Evaluation with RAG a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sundar, Koushik Manohar, Eashaan Vijay, K. Prakash, Sajay Citibank Na Jersey CityNJ United States Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

The world of Artificial Intelligence is rapidly evolving after the introduction of GEN AI. Artificial Intelligence is being adopted in many fields and to automate complex works which frees up humans to something better. In the field of education, Artificial Intelligence is used in various ways, to tailor the content to meet the needs of individual students, one to one tutoring experience, as teaching assistant, in planning, content development. The scope AI and its subfield can be leveraged further, and it is possible to use it for exam assessment, use the data from Student Management System to generate some meaningful insight on Students behavior, discipline, attendance etc. Natural Language Processing, a subset of Artificial Intelligence can be combined with other AI subsets like Large Language Model to facilitate the Teacher, Parents and Students interaction, Parents can use chatBots to receive candid feedback of their kid's performance, to generate relative performance improvement compared to previous cycle, suggestion for improvement. All it takes is to connect the Artificial Intelligence subsets with some reference system like Students database. This research analysis is to evaluate student's performance and provide feedback factoring based on four main areas like exam answer sheet, practicals, attendance and assignments. This research provides technical implementation and guidance to implement the solution using the Retrieval Augmented Generation and Large Language Model, and an architectural overview of combining data from various input modes like student information from Student Management System, text book, digital answer sheet and embedding them in the Vector database. © 2024 IEEE.

关键词： Students

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MiRGraph: A hybrid deep learning approach to identify microRNA-target interactions by integrating heterogeneous regulatory network and genomic sequences

MiRGraph: A hybrid deep learning approach to identify microR...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Pei Liu, Ying Luo, Jiawei Li, Yue Hunan University College of Computer Science and Electronic Engineering Changsha China McGill University School of Computer Science MontrealQC Canada

ISBN: (纸本)9798350386226

MicroRNAs (miRNAs) mediates gene expression regulation by targeting specific messenger RNAs (mRNAs) in the cytoplasm. They can function as both tumor suppressors and oncogenes depending on the specific miRNA and its target genes. Detecting miRNA-target interactions (MTIs) is critical for unraveling the complex mechanisms of gene regulation and promising towards RNA therapy for cancer. There is currently a lack of MTIs prediction methods that simultaneously perform feature learning from heterogeneous gene regulatory network (GRN) and genomic sequences. To improve the prediction performance of MTIs, we present a novel transformer-based multi-view feature learning method - MiRGraph, which consists of two main modules for learning the sequence-based and GRN-based feature embedding. For the former, we utilize the mature miRNA sequences and the complete 3'UTR sequence of the target mRNAs to encode sequence features using a hybrid transformer and convolutional neural network (CNN) (TransCNN) architecture. For the latter, we utilize a heterogeneous graph transformer (HGT) module to extract the relational and structural information from the GRN consisting of miRNA-miRNA, gene-gene and miRNA-target interactions. The TransCNN and HGT modules can be learned end-to-end to predict experimentally validated MTIs from MiRTarBase. MiRGraph outperforms existing methods in not only recapitulating the true MTIs but also in predicting strength of the MTIs based on the in-vitro measurements of miRNA transfections. In a case study on breast cancer, we identified plausible target genes of an oncomir. © 2024 IEEE.

关键词： Gene expression regulation

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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Hybrid Perception Analysis of World Leaders in Reddit using Sentiment Analysis

Hybrid Perception Analysis of World Leaders in Reddit using ...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Sekar, Varun Rishwandh Kannan, Thuhin Khanna Rajesh Suraj, N. Vijay, Priya Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350333817

Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active research happening worldwide. The development of NLP algorithms is of utmost importance to the advancements in the field of Artificial Intelligence. With the increasing popularity of social media and the number of hours spent on social media multiplying, people share their opinion on a wide range of topics and issues. This sudden burst of data generated by social media platforms contains a massive potential when combined with state-of-the-art NLP models, it can be leveraged to our advantage. This work builds a dataset of user comments on the top posts about political leaders on Reddit, using Reddit's API. On Reddit, extensive discussions on various topics occur daily. The end goal of this work is to rank the chosen world leaders based on their likability. To find the general likability of a public personality, we try to classify the comments collected from Reddit using Sentiment Analysis. This paper employs state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis, and combines the results to get a better final rank of the world leaders. © 2023 IEEE.

关键词： Sentiment analysis

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Integrated Cognitive Detection and Alert System for Mitigating Driver Drowsiness: A Comprehensive Approach towards Enhanced Driver Safety 15

Integrated Cognitive Detection and Alert System for Mitigati...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Banavathu, Sivaramakrishna Kukkadapu, Sri Manikanta Surya Vinay Kumar Uddagiri, Arjun Korada, Renuka Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering NTR Dt. Andhra Pradesh Mylavaram521230 India

ISBN: (纸本)9798331300579

This research paper focuses on the development of a sophisticated cognitive detection system for driver drowsiness utilizing OpenCV and Python, presenting an advanced solution to enhance driver safety. In response to the alarming statistic from Road Transport and Highways India, revealing that 22.8% of accidents result from driver drowsiness, this initiative aims to mitigate the detrimental consequences of such incidents, which lead to loss of lives, vehicle damage, road infrastructure destruction, and financial burdens on individuals. The proposed solution involves a precise driver drowsiness detection system coupled with a multi-modal alert system. Upon detecting signs of drowsiness, the system employs nuanced alert mechanisms, such as controlled subtle jerks, to prompt the driver to regain alertness. Additionally, a red-light warning is projected onto the driver's face to counteract drowsiness effectively. Notably, instead of employing abrupt braking, our system adopts a gradual deceleration strategy to ensure a smoother and safer response, minimizing the risk of sudden movements and potential collisions with the windshield. Furthermore, the integration of cruise control mechanisms contributes to reducing the overall fatality rate. This comprehensive solution not only addresses the critical issue of driver drowsiness but also strives to enhance overall road safety by leveraging advanced technology and strategic alerting methodologies. © Grenze Scientific Society, 2024.

关键词： Advanced driver assistance systems

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