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Timeless Techniques and Their Integration into the Tomes of Today’s Business Strategies 1

6th International Conference on information Systems and Management Science, ISMS 2023

作者： Barve, Amit Naval, Preeti Borah, Shivani Prakash, Febin Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Commerce and Management Assam Down Town University Assam Guwahati India Karnataka Bangalore India

ISBN: (数字)9783031664106

ISBN: (纸本)9783031664090

As businesses now look to the past for guidance, there has been a rise in interest in how to incorporate time-tested practices into their operations. This research paper provides a thorough analysis of "Timeless Techniques and Their Integration into the Tomes of Today’s Business Strategies." We provide a methodical process that compares and contrasts conventional wisdom with the requirements of today’s competitive market. We show that our suggested strategy is much better than six established approaches when it comes to creating seamless integration. There are ten main terms that stand out in this study, such as "integration," "timeless techniques," "business strategies," and more. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Integration

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Powering Next-Generation Wireless Communication With Cooperative Strategies

Powering Next-Generation Wireless Communication With Coopera...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nair, Amrutha V Panduranga Rao, M.V. Agrawal, Trapty Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Recent research has been focused on RF energy transmission and recovery as a potential strategy to greatly increase the lifespan of resource wireless networks. This method has the ability to remotely supply items with vitality that is comparatively steady and prolonged;as a result, it introduced a new methodological approach known as WPC, that had created numerous new research avenues with broad uses. In these, the creation and evaluation of effective joint plans has recently garnered a lot of study attention. The collaboration continuum communicating initiatives for behavioral connectivity networks, the collaborating crosstalk techniques forward into mobile broadband network security, and the explaining policies and procedures for wap data transmission are all highlighted in this article's general introduction of working in multiple approaches. Again once we wrap up this piece, we also highlight a number of intriguing study paths in this field. © 2023 IEEE.

关键词： Cognitive Radio Networks Collaborative Strategies Cooperative Jamming and Network Topologies Energy Harvesting Relay Selection Secure Communications Spectrum Sharing Wireless-Powered Communications Energy Transmission

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Impact of Massive Tourist and Vehicles Flow on Air and Water Quality of Uttarakhand 11

Impact of Massive Tourist and Vehicles Flow on Air and Water...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Rawat, Bhupesh Bist, Ankur Singh Nusantoro, Hardjanto Soleman, Muhammad Dede Sunarya, Po Abas Girinzio, Iqbal Desam Graphic Era Hill University Faculty of Computer Applications Uttarakhand India Graphic Era Hill University Faculty of Computer Science and Engineering Uttarakhand India University of Raharja Master of Information Technology Tangerang Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9798350305968

The degradation of air quality in hilly regions due to the influx of tourists and vehicular traffic is a significant global concern. This research focuses on assessing the impact of these factors specifically in the Uttarakhand region. While various other factors contribute to the problem, our study aims to quantify the proportion of these two inputs. To achieve this, we employ data collection methods and develop innovative deep learning techniques tailored to the issue. The novelty of our work lies in the application of these novel methods to address the problem at hand. By conducting this study, researchers have gained insights into the extent of the impact and its implications for the region. The findings of our research contribute to the existing body of knowledge and provide valuable information for policymakers, environmentalists, and stakeholders involved in addressing air quality concerns in hilly regions. © 2023 IEEE.

关键词： Air quality

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks 4

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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

作者： Srivastava, Shubhi Bhargav, H.K. Gurani, Vijayakumar Chaturvedi, Arpana Gandhi, Ankita Dharangutti, Yogesh New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Karnatak University Department of Computer Science Dharwad India New Delhi Institute of Management Department of IT/AI/ML New Delhi India Parul Institute of Engineering and Technology Department of Computer science and Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

This paper presents a novel AI-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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Kalman Filter for Radon Based Earthquake Early Detection

Kalman Filter for Radon Based Earthquake Early Detection

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2024 International Conference on Informatics Electrical and Electronics, ICIEE 2024

作者： Putra, Wahyu Sukestyastama Sunarno Mustika, I Wayan Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Yogyakarta Indonesia Universitas AMIKOM Yogyakarta Faculty of Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Nuclear Engineering and Physics Engineering Yogyakarta Indonesia

ISBN: (纸本)9798331519216

Radon measurement for earthquake prediction is challenging due to environmental noise and sensor limitations. Factors such as temperature, humidity, and soil moisture cause fluctuations in radon levels, making distinguishing seismic-related changes from normal variations difficult. In addition, sensor limitations, such as sensor resolution, can reduce the ability of early warning systems to detect precursor signals. In this study, we designed an early earthquake detection method using the Kalman Filter on radon gas concentration data collected using an ion chamber sensor at a sampling interval of 1 minute. The Kalman Filter was applied to overcome the uncertainties in sensor measurements and the radon-earthquake phenomenon. The differences between the filter predictions and the raw sensor data indicate the presence of external factors, possibly earthquake precursors. In all six analyzed events, radon anomalies were effectively captured through the difference of squares, indicating the potential of the method as an early warning system for seismic activity. The proposed method detected radon anomalies 1 to 19 days before an earthquake. © 2024 IEEE.

关键词： Kalman filters

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Image Compression Technique Using Contour Coding and Wavelet Transform in Digital Image Processing 3

Image Compression Technique Using Contour Coding and Wavelet...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Naval, Preeti Raghavendra Prasad, H.D. Nishanthi, R. Dodia, Shubham J. Sharma, Sunil Dhingra, Lovish Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

Photo compression is a vital thing in virtual photograph processing. Contour coding and wavelet transforms are exceptional techniques that may be used for image compression. Contour coding is a space-filling curve technique that encodes the picture by tracing the contours of areas inside the photograph. It makes use of a smooth space-filling curve that visits areas in a more efficient way than raster scanning. The method takes into account the similarities between nearby pixels and reduces the file length with the aid of using predictive coding. Wavelet remodel is a mathematical tool that decomposes photos into unique frequency components. The wavelet rework is based on analyzing the variations between pixels by taking the differences in their spatial correlations. This technique is superb because it can represent a photograph using fewer coefficients than other compression techniques, resulting in smaller record sizes. Universally, these two photograph compression techniques are powerful for digital image processing. They could enable faster transmission of pix and decrease storage. © 2023 IEEE.

关键词： advantageous differences scanning techniques transmission

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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