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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu, Kuricheti Suresh Venkatasubramanian, S. Kishore, S. Sree, K. Kavya Ramya Susanna, Ch.Lavanya Rao, Goda Srinivasa Lakkshmanan, Ajanthaa Pace Institute of Technology and Sciences Department of Computer Science and Engineering AndhraPradesh Valluru India Saranathan college of engineering Department of Computer Science and Business Systems Trichy India B V Raju Institute Of Technology Department of Csbs Narsapur Telangana Medak502313 India Aditya University Department of Ai&ml Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Kl University Department of Cse Vaddeswaram Andhra Pradesh Guntur India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Kattankulathur603 203 India

ISBN: (纸本)9798331529635

With the increasing prevalence of edge computing environment, there is a need to establish effective network traffic management that are able to manage vast data while also protecting the privacy of users. The proposed method enhances efficiency of bandwidth, reduce the network latency and manages computational demands without managing data privacy. This is achieved by utilizing the design of edge devices. The adaptable nature of the method enables it to react in real time to different traffic situations. It optimizes the distribution of resources and improves the overall performance of the network. Additionally, it can reduce the possibility of centralized data exposure, providing robust privacy preservation through the allowance of local model training on edge devices. Finally, this method is superior. The employment of federated learning to network traffic management provide efficiency to optimize the traffic in edge computing which is both scalable and safe. This makes it suitable for a wide variety of applications that are utilized in next generation networks © 2024 IEEE.

关键词： Federated learning

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Automated Image Captioning Systems

Automated Image Captioning Systems

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aman Swaraj Ravi Raj Aniket Shaw Richa Dubey Prianka Dey Sagarika Chowdhury Computer Science and Business Systems Narula Institute of Technology Kolkata India Computer Science & Engineering (AI& ML) Narula Institute of Technology Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the project is to design a system that leverages live camera feeds to detect objects in real time and generate concise, meaningful descriptions of the scene. The system processes the visual input, identifies key objects or actions, and produces a one-line caption summarizing the observed scenario. This caption is then saved in a text file, stored in the same directory as the program. For instance, if the live feed shows a person seated in front of the camera holding flowers, the system would generate the caption: “A person holding a bouquet of flowers,” which gets automatically written to the file. This solution integrates advanced technologies like real-time object detection and natural language generation to achieve efficient scene analysis and automated captioning. Such a system has diverse applications, including aiding visually impaired individuals, enhancing video surveillance, and improving user interaction in smart environments. By combining visual recognition with descriptive language processing, it provides an innovative way to summarize live camera feeds in a user-friendly format.

关键词： Visualization Braille Technological innovation Visual impairment Flowering plants Cameras Video surveillance Real-time systems Feeds Reliability

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Pneumonia Detection in Chest X-rays using CNN

Pneumonia Detection in Chest X-rays using CNN

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Aaryan Chothani Bharti Khemani Sachin Malave Mit Jain Jaya Gupta Department of Computer Engineering A.P. Shah Institute of Technology Thane India Department of Computer Science & Engineering (AI & ML) A.P. Shah Institute of Technology Thane India

Pneumonia is a life-threatening condition which affects people of all ages but is mostly found in infants and older adults. It is caused by a variety of factors including bacteria, viruses and fungi and causes infections in one lung or both. The diagnosis of Pneumonia is usually facilitated using chest X-rays. This diagnosis becomes difficult in cases where access to expert radiologists or doctors to discover the disease is unavailable. Hence, using a system which makes use of powerful Machine Learning and Deep Learning techniques in order to make the diagnosis of Pneumonia in chest X-rays easier is more feasible. This problem has seen tremendous advancements in recent times with the use of various computer Vision and ml methodologies. In this work, we have showcased the use of multiple such methods in order to create a complete system based on the purpose of detecting Pneumonia in chest X-rays. The integral component of the system is a Convolutional Neural Network (CNN) model, trained on a pre-processed dataset of chest X-rays. CNN has become the most used Deep Learning algorithm for computer vision problems due to its strength of dealing with images.

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Prediction of aiDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The aiDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/aiDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/aiDS optimizers

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On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm 41

On the Emergence of Cross-Task Linearity in Pretraining-Fine...

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41st International Conference on Machine Learning, ICml 2024

作者： Zhou, Zhanpeng Chen, Zijun Chen, Yilan Zhang, Bo Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China Shanghai Artificial Intelligence Laboratory China Computer Science and Engineering University of California San Diego United States

The pretraining-finetuning paradigm has become the prevailing trend in modern deep *** this work, we discover an intriguing linear phenomenon in models that are initialized from a common pretrained checkpoint and finetuned on different tasks1, termed as Cross-Task Linearity (CTL).Specifically, we show that if we linearly interpolate the weights of two finetuned models, the features in the weight-interpolated model are often approximately equal to the linear interpolation of features in two finetuned models at each *** provide comprehensive empirical evidence supporting that CTL consistently occurs for finetuned models that start from the same pretrained *** conjecture that in the pretraining-finetuning paradigm, neural networks approximately function as linear maps, mapping from the parameter space to the feature *** on this viewpoint, our study unveils novel insights into explaining model merging/editing, particularly by translating operations from the parameter space to the feature ***, we delve deeper into the root cause for the emergence of CTL, highlighting the role of *** released our source code at https://***/zzp1012/Cross-Task-Linearity. Copyright 2024 by the author(s)

关键词： Deep learning

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Towards a Personalized LLM-Based Daily Edge Memory aid 15

Towards a Personalized LLM-Based Daily Edge Memory Aid

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Ha, Sangil Ko, Dongbeom Lim, Joon-Ho Kang, Sungjoo Kim, Hyeon Soo Ai Lab Tutorus Labs Inc. Deajeon Korea Republic of Deajeon Korea Republic of Department of Computer Science and Engineering Deajeon Korea Republic of

ISBN: (纸本)9798350364637

This paper introduces a personalized memory aid system that combines a high-performance cloud-based large language model (LLM) with a low-power edge device running a small language model (sLM) to enhance user productivity by processing and summarizing daily conversations in real-time. The system optimizes efficiency and user experience while ensuring privacy through localized data handling. By combining cloud and edge resources, the system provides scalable, real-time memory support without compromising data security. The research highlights the system's architecture, its role in protecting user data, and its potential to seamlessly integrate into daily life, offering a sustainable and efficient approach to personalized artificial intelligence applications. © 2024 IEEE.

关键词： Cloud computing architecture

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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Sentiment Analysis of Bitcoin data by tweets through Naive Bayes

Sentiment Analysis of Bitcoin data by tweets through Naive B...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Padmalatha, E. Sheral, Sailekya Dhanush, K. Samveeth, S. Datta, B.Ruchi Krishna, K.Tarun CBIT Department of Computer Science Telangana Hyd India University of Exeter Department of Computer Science Exeter United Kingdom GITAM University Department of AI & ML Telangana Hyd India Deparment of DS & AI ICFAI University Telangana Hyd India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Bitcoin, as one of the most popular cryptocurrencies, is notorious for its concerning volatility on any given day, which leads to people having mixed feelings about it. This paper examines the opinions of people all over the world using data gathered from the social media platform Twitter, to decide if investing in bitcoin is a good idea for future. The opinions are analyzed and classified using the Bernoulli and Multinomial Naive Bayes model through sentiment analysis, the models implemented yielded 87 and 86 percent of accuracies respectively. Furthermore, the variation in sentiment over time is comparedto the variation in bitcoin price over the same period. © 2022 IEEE.

关键词： Bitcoin

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An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

An Empirical Analysis on Spatial Reasoning Capabilities of L...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs' spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our new benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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