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Spatial Modulation Techniques for Improved ISAC Throughputs

Spatial Modulation Techniques for Improved ISAC Throughputs

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Suman, Sanjay Kumar Rajalakshmi, B. Khan, Irfan Alekhya, V. Lakhanpal, Sorabh Ali, Abduljabbar A. St. Martin's Engineering College Telangana Secunderabad India New Horizon College of Engineering Department of Computer Science Bangalore India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India Lovely Professional University Phagwara India National University of Science and Technology College of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This study introduces 'Adaptive Spatial Modulation for Enhanced ISAC Throughputs (ASM-ISAC)' to speed up Intelligent Space-Air-Ground Communication (ISAC). An adaptive spatial modulation technique, CSD for cooperative spatial diversity, and EESM-ISAC for energy-efficient spatial modulation are used. To boost communication throughput and reduce bit error rates, ASM-ISAC adjusts modulation and spatial patterns in real time. Cooperation between close ISAC nodes improves performance in CSD. EESM-ISAC develops energy-efficient communication with restricted resources. This research evaluates ASM-ISAC against earlier approaches like 'Conventional Modulation Scheme for ISAC (CMS-ISAC)'. The test evaluates frequency efficiency, error performance, latency, complexity, energy efficiency, and security. ASM-ISAC outperforms CMS-ISAC in several aspects, suggesting it might increase ISAC throughput. © 2024 IEEE.

关键词： Adaptive modulation

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Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramu, K. Nijhawan, Ginni Lalitha, G. Praveen Asha, V. Fakher, Ahmed J. Velammal Institute of Technology Dept. of Cse TN Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India New Horizon College of Engineering Department of Computer Applications Bangalore India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous. © 2024 IEEE.

关键词： Differential privacy

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

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computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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CoRAL: a Context-aware Croatian Abusive Language Dataset 2

CoRAL: a Context-aware Croatian Abusive Language Dataset

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2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2022

作者： Shekhar, Ravi Karan, Mladen Purver, Matthew Cognitive Science Research Group School of Electronic Engineering & Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jo ef Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9781959429043

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderatorsby either automatically classifying the examples or allowing the moderators to prioritizewhich comments to consider first. However,the concept of inappropriate content is oftensubjective, and such content can be conveyedin many subtle and indirect ways. In this work,we propose CoRAL1 a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and relianceon local and global context. We show experimentally that current models degrade whencomments are not explicit and further degradewhen language skill and context knowledgeare required to interpret the comment. © AACL-IJCNLP *** rights reserved

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An Enhanced Haar Cascade Face Detection Schema for Gender Recognition

An Enhanced Haar Cascade Face Detection Schema for Gender Re...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Sridhar, Psvs Chithaluru, Premkumar Kumar, Sunil Cheikhrouhou, Omar Hamam, Habib Computer Science and Engineering Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India National School of Engineers of Sfax University of Sfax Ces Laboratory Sfax3038 Tunisia University de Moncton Faculty of Engineering MonctonNBE1A3E9 Canada International Institute of Technology and Management Commune d'Akanda Libreville Gabon School of Electrical Engineering University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg2006 South Africa Spectrum of Knowledge Production and Skills Development Sfax Tunisia

ISBN: (纸本)9798350347050

Gender identification software is becoming increasingly popular in the age of machine-to-machine interactions, as the need for person-aligned, effective, and ethical systems become more apparent. In this age of machine-to-machine interactions, automated gender identification software is becoming more popular. The bulk of the systems for gender identification that were examined used either textual or audiovisual data as input to determine their gender. Many different approaches to automatically detect gender based on traits gathered from people's bodies and/or behaviors have been suggested, and there are simply too many to include here. The accuracy of automated gender identification, on the other hand, has always been a topic of worry or a source of criticism. Initial face identification is accomplished by the use of the Haar Cascade face detection method, which is based on the Viola-J ones face recognition algorithm. Facial characteristics such as the eyes, mouth, and nose are then identified. Second, using the Viola-Jones face detection algorithm, the faces and facial characteristics such as the eyes, mouth, and nose are recognized, and the results are shown. In order to increase the accuracy of gender identification, adaptive filters are employed to reduce the amount of noise present before the detection process is carried out. The facial qualities that have been gathered are utilized as input or test data for the neural network, which is subsequently trained on these characteristics. It is intended that the neural network acquires the features of the genders and then functions as a classifier when it comes to detecting them. Instead of saying it another way, a neural network known as Keras is used for feature extraction and gender identification, which is free and open-source software. © 2023 IEEE.

关键词： Feature extraction

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knowledge-augmented Self-training of A Question Rewriter for Conversational knowledge Base Question Answering

Knowledge-augmented Self-training of A Question Rewriter for...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ke, Xirui Zhang, Jing Lv, Xin Xu, Yiqi Cao, Shulin Li, Cuiping Chen, Hong Li, Juanzi School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Department of Computer Science and Technology Tsinghua University Beijing China

The recent rise of conversational applications such as online customer service systems and intelligent personal assistants has promoted the development of conversational knowledge base question answering (ConvKBQA). Different from the traditional single-turn KBQA, ConvKBQA usually explores multi-turn questions around a topic, where ellipsis and coreference pose great challenges to the single-turn KBQA systems which require self-contained questions. In this paper, we propose a rewrite- and-reason framework to first produce a full-fledged rewritten question based on the conversation history and then reason the answer by existing single-turn KBQA models. To overcome the absence of the rewritten supervision signals, we introduce a knowledge-augmented self-training mechanism to transfer the question rewriter from another dataset to adapt to the current knowledge base. Our question rewriter is decoupled from the subsequent QA process, which makes it easy to be united with either retrieval-based or semantic parsing-based KBQA models. Experiment results demonstrate the effectiveness of our method and a new state-of-the-art result is achieved. The code and dataset are available online now. © 2022 Association for Computational Linguistics.

关键词： knowledge based systems

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Review of advanced drying techniques: a path to lower greenhouse gas emissions in agriculture

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Discover Sustainability 2025年第1期6卷 1-26页

作者： Kaveh, Mohammad Abbaspour-Gilandeh, Yousef Nadimi, Mohammad Shahbazi, Feizollah Paliwal, Jitendra Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq Department of Biosystems Engineering College of Agriculture and Natural Resources University of Mohaghegh Ardabili Ardabil Iran Department of Biosystems Engineering University of Manitoba Winnipeg Canada Department of Biosystems Engineering Lorestan University Khoramabbad Iran Department of Applied Computer Science The University of Winnipeg Winnipeg Canada

The agricultural sector has one of the largest carbon footprints among all industries due to the extensive use of fossil fuels, chemical fertilizers, and pesticides. Over the past century, agricultural mechanization has remarkably increased greenhouse gas (GHG) emissions, contributing to global warming and climate change. Among these gases, carbon dioxide (CO2) is the most abundant. Drying is a crucial and widely used method for preserving agricultural products, with broad applications in the food industry. Recent advancements in drying technology offer promising alternatives that enhance product quality, reduce energy use, and mitigate GHG emissions, thus promoting environmental sustainability. This review explores some of the most promising drying techniques that will shape the future of agricultural processes. Efficient and innovative drying of agri-food products can be achieved by hybridizing conventional techniques like hot-air, microwave, infrared, fluid bed, continuum, vacuum, and refractance window drying with pre-treatments such as ultrasound (US), pulsed electric fields (PEF), blanching, and cold plasma (CP). The combined use of these modalities can decrease GHG emissions while producing high-quality, nutritionally rich products. Our synthesis of published information also proposes research and development strategies to mitigate GHGs during the drying process.

关键词： Drying Environmental sustainability Greenhouse gases Hybrid dryers Pre-treatment

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Enhanced Deep Learning Approach for Software Defect Forecasting

Enhanced Deep Learning Approach for Software Defect Forecast...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Anju A. J J.E. Judith Department of Computer Science Engineering Eranad Knowledge City Technical Campus Kerala India Department of Computer Science Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

In the realm of defect prediction in complex systems, the integration of advanced machine learning techniques holds promise for enhancing accuracy and efficiency. This research introduces a novel approach by combining a Deep Adaptive Neural Network (ARNN) with the optimization capabilities of Cuckoo Search (CSO) for defect and non-defect data classification. Th e proposed model leverages the temporal dependencies within the data using a deep ARNN architecture, allowing for the exploration of intricate patterns and correlations over time. To optimize the model's performance, the Cuckoo Search Optimization algorithm is employed. CSO, inspired by the reproductive behavior of cuckoos, offers an effective mechanism for fine-tuning the parameters of the deep ARNN, optimizing its ability to discern subtle variations between defect and non-defect instances. The efficacy of the proposed approach is evaluated on benchmark datasets, showcasing its capability to achieve superior predictive accuracy compared to conventional methods.

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Supply Chain Strategies Supported By Blockchain Technology: Open Problems and Challenges

Supply Chain Strategies Supported By Blockchain Technology: ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sumit Kumar Rana Arun Kumar Rana Sardar M N Islam Naz Ashutosh Kumar Singh Bharat Bhushan Department of Computer Science and Engineering Panipat Institute of Engineering and Technology Panipat Haryana India Department of Computer Science & Engineering Galgotia college of engineering Knowledge Park I Greater Noida Uttar Pradesh India ISILC Victoria University Melbourne Australia Department of Computer Applications Kurukshetra University NIT Kurukshetra India Department of Computer Science & Engineering Sharda Universi Knowledge Park III Greater Noida India

The most prevalent issue in the supply chain ecosystem is introducing transparency to the system. Another issue is fostering trust among various stakeholders to support successful process execution. Depending on the domain and the stakeholders, the supply chain process may change. Thus, solving the issue is challenging. Blockchain technology, sometimes referred to as distributed ledger technology, is beneficial in many ways in a variety of industries, including finance and healthcare. Blockchain based model can resolve the issues of traditional supply chain ecosystem. It creates a decentralized and transparent system where every decision is taken with the help of consensus of the associated stakeholders. This supports an immutable ledger so unauthorized modification is not supported. In this article, we address the issues that the supply chain ecosystem faces as well as the benefits of blockchain technology in resolving these issues. As well as some of the remaining challenges and opportunities, other aspects of blockchain technology are explored that will enable it flourish in future supply chains.

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Towards Portable Identities in the Matrix Protocol 42

Towards Portable Identities in the Matrix Protocol

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42nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2022

作者： Ihle, Cornelius Deifub, Fabian Schubotz, Moritz Gipp, Bela University of Göttingen Department of Computer Science Göttingen Germany Data & Knowledge Engineering University of Wuppertal Wuppertal Germany Mathematics Fiz Karlsruhe Berlin Germany

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

In this paper we present a cryptographic challenge-response authentication mechanism to enable portable identities within the Matrix protocol. Online instant communication systems like Matrix connect people in a convenient and cost-effective manner. However, most of today's communication infrastructure relies on inherently centralized infrastructure. Matrix currently, takes a federated approach instead. However, to fully decentralize the Matrix network, further actions must take place. Among others, a user's identity must be disconnected from the server they used to sign up and happen to communicate on. As part of this effort, a challenge-response authentication mechanism has been implemented that allows user IDs to be usable on any server, even if the a user's homeserver becomes unreachable. © 2022 IEEE.

关键词： Authentication

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