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Alpha-x Framework for Assessing Network Robustness in Nakagami-m Fading and Co-Channel Interference Environments

Alpha-x Framework for Assessing Network Robustness in Nakaga...

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Delhi Section Conference (DELCON), IEEE

作者： Deepak Upadhyay Karan Siwach Shiv Ashish Dhondiyal Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This article explores how the Alpha-x framework can improve network robustness under Nakagami-m fading and co-channel interference (CI), The study evaluates network performance using key metrics such as outage probability, spectral efficiency, and capacity degradation. The simulations show a 50% reduction in outage probability, a 20% boost in spectral efficiency, and a 33.3% lower capacity degradation compared to traditional models. These results demonstrate how the Alpha-x framework enhances network performance by adapting to various fading conditions and interference levels in wireless communication systems. The framework not only improves reliability and efficiency but also shows practical relevance in network planning and management. This can potentially lead to better user experiences and increased operational resilience. Future research could focus on optimizing the underlying algorithms and testing their performance on a larger scale in real-world environments. As telecommunications systems evolve, frameworks like Alpha-x could play a crucial role in ensuring reliable and efficient wireless networks.

关键词： Fading channels Degradation Adaptation models Spectral efficiency Probability Robustness Power system reliability Telecommunication network reliability Interchannel interference Capacity planning

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Retrieval Augmented Generation for Relational Mapping of Resume Data for Improved Analysis

Retrieval Augmented Generation for Relational Mapping of Res...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Vazzula Manish Yugandhar Manchala Sudeep Banerjee Chopra Muddam Siddartha Kothi Yashwanth Reddy Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Dept. of Computer Science (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Analysis of resume data is often a critical and time-intensive process that requires extensive metadata. However, a lack of consistency and scalability often leads to inefficiencies and errors in the recruitment process. Tools like Applicant Tracking Systems (ATS) often struggle with the contextual understanding of resume data, as they evaluate each resume in isolation without considering its relations to other similar resumes. We proposes the implementation of a Retrieval-Augmented Generation (RAG) architecture to create a vector relational mapping of resume data based on several curated parameters, such as skills, experiences, and education. This approach enables the system to understand the contextual relations between different resumes, helping the system evaluate them more effectively within a broader context, and improve recruitment outcomes by reducing biases and increasing efficiency.

关键词： Social networking (online) Soft sensors Resumes Retrieval augmented generation Software as a service Transforms Natural language processing Vectors Recruitment Software development management

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Exploring the Relationship between Rent and Flat Prices Through Random Forest and Grid Search

Exploring the Relationship between Rent and Flat Prices Thro...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Ghosh, Joyita Maji, Kalyan Mzili, Toufik Roy, Priyanka Chakraborty, Monalisa Gupta, Subir W.B Haldia India India Chouaib Doukkali University Dept of Computer Science El Jadida Morocco Dr. B. C. Roy Engineering College Dept. of It WB Durgapur India Dr. B. C. Roy Engineering College Dept. of Cse WB Durgapur India

ISBN: (纸本)9798350365306

This paper explores the critical relationship between flat rents and prices, an important metric reflecting the state of the housing market and its underlying economic factors. The study introduces predictive models based on Random Forest algorithms, refined through Grid Search optimisation, to provide accurate market forecasts. Such predictive capabilities are crucial for policymakers, investors, and developers, who require reliable data for strategic decision-making and policy formulation. The research methodology employs Random Forest for its robust handling of complex datasets, with Grid Search utilised to calibrate the model parameters for enhanced precision. The resulting models are rigorously evaluated, achieving a training accuracy of 0.948 and testing accuracies of 0.745 for price prediction and 0.885 for rent prediction, indicating predictive solid performance. Low Log Loss metrics further validate the models' reliability. The study identifies the model's strengths and areas for refinement through detailed visualisations - accuracy comparisons, correlation heatmaps, and residual distribution plots. The residual plots, in particular, indicate a better generalisation capability in rent forecasting compared to flat price predictions, which exhibit minor overfitting. The findings provide actionable insights, demonstrating the efficacy of Random Forest and Grid Search in capturing the complex dynamics of flat rents and prices. The success of these models indicates their potential for broader application across different regions and market conditions, suggesting a valuable asset for stakeholders in achieving a balanced and fair housing market. This paper aims to contribute to the literature by offering a novel, data-driven perspective on market analysis and economic forecasting within the real estate sector © 2024 IEEE.

关键词： Prediction models

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Distributed IRSs Mitigate Spatial Wideband & Beam Split Effects

Distributed IRSs Mitigate Spatial Wideband & Beam Split Effe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： L. Yashvanth Chandra R. Murthy Bhaskar D. Rao Dept. of Electrical Communication Engineering Indian Institute of Science Bangalore India Dept. of Electrical and Computer Engineering University of California San Diego USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the IRS phase shifters fail to form a beam at the desired user equipment (UE) over the complete bandwidth (BW) allotted to that UE, in turn reducing the system throughput. This paper proposes a distributed IRS design that aids in naturally combating the SW/BSP effects by parallelizing the spatial delays across the multiple IRSs. Specifically, we propose to split a single large IRS into multiple smaller IRSs and distribute them over the geographical area. Then, by specifying the maximum permissible number of elements at each IRS, we ensure that the B-SP remains within a specified tolerance level. Finally, we derive the sum-rate and show that the proposed solution yields a nonzero rate over the whole BW of operation and circumvents the SW/B-SP effects. We verify our findings numerically and illustrate the low complexity of the proposed method compared to state-of-the-art techniques.

关键词： Array signal processing Phase shifters Apertures Throughput Acoustics Delays Complexity theory Acoustic beams Speech processing Wideband

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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

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Intelligent Computing and Emerging Communication Technologies (ICEC), International Conference on

作者： Valluri Ayyapa Kesava Sriram Kothamasu Priyusha Killaru Saadhivik Muddana Suresh Aala Vamsi Gutha Mukkara Prasana Kumar Sunil Chinnadurai Dept.of Electronics and Communication Engineering SRM University AP India Dept. of Computer Science and Engineering SRM University AP India Dept. of Computer Science SRM University AP India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research.

关键词： Measurement Accuracy Computational modeling Mortality Feature extraction Reliability Root mean square Cancer Hyperspectral imaging Synthetic data

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A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

A Wearable RF Energy Harvester Based on Spoof Surface Plasmo...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Liu, Zhu Li, Jinxin Xiao, Pei Hunan Normal University Dept. Physics and Electronics Changsha China Hunan University Dept. Computer Science and Electronic Engineering Changsha China Hunan University Dept. Electrical and Information Engineering Changsha China

ISBN: (纸本)9781665452366

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 μ W under a power density of 2.75 μ W/cm2 when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network. © 2022 IEEE.

关键词： Antenna arrays

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IoT Applications in Blockchain Technology 1

IoT Applications in Blockchain Technology

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Bale, Ajay Sudhir Biswas, Ankur Malik, Suraj Uchoi, Enjula Soni, Sabhyata Uppal Soni, Arun New Horizon College of Engineering Dept. of Ece Bengaluru India Adamas University Dept. of Comp. Sc. & Engg. Kolkata India Meerut India Lovely Professional University Computer Science and Engineering Department India Panjab University Uiet Department of Electrical & Electronics Engineering India Mohali India

ISBN: (纸本)9798350341737

Securing the data through blockchain might be crucial to enhancing the IoT network, since data is an especially important part of the system. Secure storage of the information generated by the IoT network's intelligent gadgets is made possible via the integration of blockchain technology. For blockchain to be widely used in the IoTs, it must first conquer technological and financial barriers. To make the most of the IoT, blockchain technology may require some tweaking. The upcoming Internet of Things (IoT) technical breakthrough could involve this. This work emphasizes on bringing on board the recent trends of IoT applications utilizing Blockchain technology. The challenges faced in doing so are also outlined. We're hoping this work might help elucidate the state of blockchain studies and provide a road map for what comes next. © 2023 IEEE.

关键词： Supply chains

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Navigating Innovation within a Contractor’s Business Model: A System Dynamics Approach

Navigating Innovation within a Contractor’s Business Model:...

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IEEE International Conference on System of Systems engineering (SoSE)

作者： Mariam Elazhary Cihan Dagli Islam H. El-Adaway Ph.D. Graduate Student Dept. of Civil Architectural and Environmental Engineering Missouri Univ. of Science and Technology Rolla MO Professor Dept. of Engineering Management and Systems Engineering Missouri Univ. of Science and Technology Rolla MO Professor Dept. of Civil Architectural and Environmental Engineering Dept. of Engineering Management and Systems Engineering Missouri Univ. of Science and Technology Rolla MO

ISBN: (数字)9798350365917

ISBN: (纸本)9798350365924

Innovation is essential in the construction industry, but current practices often focus narrowly on specific aspects rather than taking a comprehensive approach. To address this, systematic innovation through business model innovation (BMI) is proposed to enhance overall performance. Despite increasing interest in BMI research, it remains in its early stages, lacking quantitative analysis of its impact on business model components. This paper develops a framework using system dynamics to assess changes to a construction company’s BM when faced with a variety of innovations across various projects. This shall be accomplished by (1) establishing factors and their relationships that define a contractor’s BM over time, (2) modeling the said factors using AnyLogic using synthetic data, and (3) evaluating the emergent behavior of BM’s value creation. The study found that the impact of innovation on a contractor’s long-term value creation is not linear; it varies based on when and what type of innovation is implemented. This complexity arises from the trade-offs between cost reductions and additional expenses associated with innovation. The paper introduces a comprehensive framework to assess how innovation affects a contractor’s business model, enabling practitioners to understand better and manage their long-term value-creation strategies.

关键词： Technological innovation Systematics System dynamics Statistical analysis Navigation Data models Modeling

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Malware Detection across Clusters

Malware Detection across Clusters

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Manjushree C V A.N. Nandakumar Dept. of information science & Engineering Vemana Institute of Technology India Dept. of Computer science & Engineering City Engineering College India

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures.

关键词： Deep learning Knowledge engineering Java Simulation Neural networks Software algorithms Clustering algorithms

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Network-based Intrusion Detection Datasets: A Survey 23

Network-based Intrusion Detection Datasets: A Survey

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Ahmed, Lubna Ali Hassan Hamad, Yahia Abdalla Mohamed Abdalla, Ahmed Abdallah Mohamed Ali Sudan University of Science and Technology Dept. of Electronics Engineering Khartoum Sudan Sudan University of Science and Technology Dept. of Computer Networks and Systems Khartoum Sudan

ISBN: (纸本)9798350320244

The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fields that could highly contribute to cyber-Attacks detection and mitigation. Datasets are necessary for NIDS training and evaluation. This paper provides a comprehensive survey of publicly available NIDS datasets. The study covers the most used NIDS datasets, each dataset is briefly described along with its main properties and shortcomings. Moreover, the paper highlights the most important dataset characteristics and provides a clear datasets comparison accordingly. Finally, the paper discusses observations and provides some recommendations to help researchers identify future work. © 2022 IEEE.

关键词： Network security

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