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A Comparative Analysis: Detection of Human Edited Images Using Machine Learning Techniques

A Comparative Analysis: Detection of Human Edited Images Usi...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Swapnita Pandey Ayush Singh Ayush Rawat Sumit Pundir Sakshi Pundir Dept. of Computer Science and Engineering Graphic Era Deemed To Be University Dehradun India Dept. of Management and Studies Graphic Era Hill University Dehradun India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The rise of digital image manipulation tools using artificial intelligence approaches has become widespread in recent years, blurring the line between authentic and altered images. To address this, the proposed paper introduces a method employing a convolutional neural network (CNN) preceded by Error Level Analysis (ELA) for detecting human-edited images. The work also makes a comparative analysis between the various models (Custom-built CNN, VGG16, Xception) and their ensemble combinations using a variety of performance metrics. Though the other models and their ensemble combinations also achieved an accuracy above 87% the highest work accuracy of 92.70% was attained by the model built by the ensemble combination of the custom-built CNN with the Xception model. Subsequently, the use of a large dataset and different dataset splitting ratios ensures the robustness and efficiency of the work in pinpointing human-edited images, thus standing testament to the promise of fortifying the credibility of digital content.

关键词： Measurement Analytical models Accuracy Digital images Neural networks Machine learning Network security Data science Robustness Convolutional neural networks

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An Empirical Analysis of Relationship between Economic Growth and Freedom: A Cross Country Study using Machine Learning Approach

An Empirical Analysis of Relationship between Economic Growt...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Shivkumar S Deepa Gupta Deepika M G Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Dept. of Management Amrita School of Management Bengaluru Amrita Vishwa Vidyapeetham India

While there is sufficient exploration on the role of labour, capital and technology as determinants of economic growth, less is analysed on the role of economic and political freedom as factors influencing the economic growth of a country. The economic growth of some countries is high and in some low. The reason for this variation has been a matter of discussion in economic literature. In this paper, the relationship between economic growth and economic freedom along with labour and capital is analysed. The multi-linear regression model is used to estimate the relationship. The results obtained indicate that there exists a significant relationship between economic growth, economic freedom, labour and capital. The R 2 score was 0.929 which states that the dependent variable is explained well by the independent variables.

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Meteorology Predicting Technology Based on Artificial Intelligence Algorithms

Meteorology Predicting Technology Based on Artificial Intell...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Gulnara Abitova Daniyar Mukashev Elvira Aitmukhanbetova Mira Rakhimzhanova Gulzhan Uskenbayeva Korlan Kulniyazova Dept electrical engineering and computer science Binghamton University Binghamton USA Dept. of computer engineering Astana IT University Astana Kazakhstan Dept. system analysis and management L.N. Gumilyov Eurasian National University Astana Kazakhstan

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage.

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From Pixels to Paragraphs: Extraction of Text from Medical Reports using LLMs, Systematic Literature Review

From Pixels to Paragraphs: Extraction of Text from Medical R...

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Foundation and Large Language Models (FLLM), International Conference on

作者： Muhammad Sadiq Ramsha Tariq Anayat Ullah Jeneen Panezai Dept. of Computer Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan Department of Microbiology Balochistan University of IT Engineering and Management Sciences Quetta Pakistan Department of Electronic Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

ISBN: (数字)9798350354799

ISBN: (纸本)9798350354805

Large language models are emerging very rapidly in applications covering nearly all fields including healthcare. In healthcare, these are commonly used for image to text, information extraction and generation and finding results from the medical notes and images. In spite of their extraordinary applications in healthcare, a standard and comprehensive systematic literature review (SLR) in this domain is lacking. However, there are quite a few systematic literature reviews in the field of large language models (LLMs) approaching the medical field. For that reason, this paper evaluates the literature to gather the relevant studies regarding LLMs in the medical field. The SLR process including a detail search was utilized to evaluate the research papers published between 2018 to date i.e. 2024, and a total of 82 studies, after rigorous screening, were included in this SLR. To wisely categorize these studies, consequently, we employed specific quality assessment criteria. Out of these 82 significant studies, only 4% were recognized to have utilized standard SLR guidelines. The results were presented in the form of charts and tables covering all the important aspects of meta data related to these selected studies. The quality assessment in screening these studies is also carried out in a very meticulous manner. Finally, an extensive discussion is carried out on formulated research questions in the methodology section and the suitable answers are discussed there. This paper therefore fulfills the gap for the scholars working on LLMs in the medical domain.

关键词： Large language models Medical services Metadata Information retrieval Quality assessment Standards Systematic literature review Biomedical imaging Guidelines

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Scope of IoT in Emerging engineering Technology during Online Education 14

Scope of IoT in Emerging Engineering Technology during Onlin...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Veeraiah, Vivek Kotti, Jayasri Jain, Vipin Sharma, Tripti Saini, Sumit Gupta, Ankur Adichunchanagiri University Dept. of R & D Computer Science Karnataka Mandya India Gmr Institute of Technology Dept. of Cse AP Vizianagaram India Teerthanker Mahaveer University Dept. of Management Uttar Pradesh Moradabad India Rungta College of Engineering & Technology Department of Cse Chhattisgarh Bhilai India Central University of Haryana School of Engineering & Technology Haryana Mahendergarh India Vaish College of Engineering Department of Cse Haryana Rohtak India

ISBN: (纸本)9798350335095

Physical objects with built-in sensors, processors, software, and other technologies that can communicate with one another and share data via the web are what the IoT refers to. Diverse devices that may cause security risks to your business, a new engineering method based on IoT safeguards Internet devices & networks they connect to against assaults and breaches. The Internet of Things (IoT) can be protected against intrusion, its vulnerabilities can be monitored, and any existing holes may be patched with the help of cutting-edge engineering practises. IoT security guarantees the solution's availability, integrity, and privacy. With the help of mobile devices and the IoT, schools can better protect their campuses, keep an eye on vital resources, and improve students' access to information. In addition, teachers may use this technology to create 'smart lesson plans,' which are more engaging for students than the traditional lesson plans of the past. One of the strategies advocated in this study is the use of IoT to protect the educational sector against cyber threats. Existing research is being compared to proposed research in terms of safety, reliability, and effectiveness. © 2023 IEEE.

关键词： Internet of things

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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Silchar Subsection Conference (SILCON), IEEE

作者： Piyush Saha Sumon Ghosh Dinabandhu Bhandari Dept. of Electrical Engineering Calcutta Institute of Engineering and Management Kolkata India Dept. of Computer Applications Heritage Institute of Technology Kolkata India Dept. of Computer Science Heritage Institute of Technology Kolkata India

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field.

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FPGA-based Automatic Pill Dispenser using Decision Tree Classifier

FPGA-based Automatic Pill Dispenser using Decision Tree Clas...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Shaina Suresh Dev, Rahul Singh, Brajesh Kumar Agarwal, Nishi Kumar, Sandeep G L Bajaj Institute of Technology and Management Department of Electronics and Communication Uttar Pradesh Greater Noida India G.L. Bajaj Institute of Technology and Management Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Galgotia College of Engineering and Technology Dept. of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Dev Bhoomi Uttarakhand University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350378092

Accurate pill dispensing is crucial for ensuring effective medication management, particularly for elderly individuals and patients with chronic conditions. In this research, we propose an FPGA-based automatic pill dispenser utilizing a decision tree classifier. The decision tree algorithm classifies pills based on their shape and size features, facilitating precise dispensing at designated times. Our methodology involves training the decision tree classifier using a sample dataset of pill characteristics. Through entropy calculations and information gain analysis, we derive a decision tree tailored to the dataset. The FPGA implementation enables real-time pill dispensing, offering advantages such as low power consumption and parallel processing. Our findings demonstrate the efficacy of the proposed approach in accurately dispensing pills, addressing the critical need for reliable medication administration systems. © 2024 IEEE.

关键词： Automatic Pill Dispenser Decision Tree Classifier Entropy Calculation FPGA (Field-Programmable Gate Array) Machine Learning

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Data-Driven Decision Making in Supply Chain management

Data-Driven Decision Making in Supply Chain Management

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Gaurav Yadav Deepa Bakhshi Syed ShafiqueUddin Namish Mehta K. K. Garg Saurabh Pratap Singh Rathore Dept. of Computer Science J.S. University Shikohabad India Dept. of Management Bhabha University Bhopal India Dept. of Mechanical Engineering Bhabha University Bhopal India Dept. of Commerce Lingayas Vidyapeeth Faridabad India Dept. of Management ICAPSR New Delhi India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In supply chain management, data-driven decision-making makes use of real-time data and sophisticated analytics to boost productivity, cut expenses, and boost overall performance. Businesses can obtain important insights into their supply chain operations by combining big data, machine learning, and predictive analytics. Better demand forecasting, inventory level optimization, and proactive detection of possible disruptions are made possible by this strategy. Utilizing data analytics tools makes it easier to analyze enormous volumes of data from different sources, including manufacturers, suppliers, and consumers, in order to present a thorough picture of the supply chain. Making well-informed decisions about distribution, production scheduling, and procurement is aided by these insights. Additionally, data-driven decision-making facilitates the adoption of just-in-time inventory procedures, which cut down on excess inventory and holding expenses. By offering early warning indicators, it also helps identify and reduce risks like supply chain bottlenecks or supplier failures. Furthermore, dynamic modifications to production and distribution schedules are made possible by real-time data, guaranteeing that the supply chain stays flexible and sensitive to shifts in the market. This strategy improves cooperation and communication between supply chain participants, which boosts efficiency and coordination. In the end, data-driven decision-making in supply chain management creates a more competitive and robust supply chain that can satisfy consumer demands while lowering expenses and hazards. Data in supply chain management will play an ever-more-important role in promoting operational excellence and attaining sustainable growth as businesses continue to embrace digital technologies and data-driven strategies.

关键词： Productivity Procurement Schedules Supply chain management Supply chains Decision making Real-time systems Vehicle dynamics Predictive analytics Business

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Davide Mascolo Lorenzo Farina Manuela Petti MSc Program in Data Science Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy.

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