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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： S. P. Santhoshkumar R. Karthika N. Vijayakumar S. Gajalakshmi K. Jayanthi S. Hariharasudhan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology School of Computing Rathinam Technical Campus Coimbatore India Computer Science and Engineering Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields.

关键词： Uncertainty Accuracy Image recognition Predictive models Probabilistic logic Natural language processing Planning Artificial intelligence Robots Optimization

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A Comprehensive Survey on MRI Images Classification for Brain Tumor Identification using Deep Learning Techniques 3

A Comprehensive Survey on MRI Images Classification for Brai...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Pranitha, Kondra Vurukonda, Naresh Nayak, Rudra Kalyan Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Vit Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Kothrikalan MP Sehore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

A brain tumor, a disease that can be fatal, threatens the most valuable human life, and it is a difficult work for a doctor to diagnose the tumor accurately and promptly. The aberrant proliferation of brain cells results in a condition known as a brain tumor. The rarity and variety of tumors make it difficult to gauge a patient's prognosis after being diagnosed with one. Manual identification is a time-consuming and difficult method that can lead to inaccuracies in the results of tumor identification using Magnetic Resonance Imaging (MRI). MRI images play a vital role in tumor site determination. These limits necessitate the use of computer-assisted techniques. It is common practice to utilize MRI scans to identify a variety of tissue abnormalities, to look for tumors, and to assess whether a tumor is still present or returning. Deep learning (DL) algorithms are being utilized in neuroimaging to detect brain cancers using MR images as artificial intelligence advances. It is critical that medical photographs be processed to aid in the identification of various disorders. The information and expertise of the physician are critical in the diagnosis of brain tumors. Physicians need an automated method to detect and classify brain cancers. MRI-based segmentation of brain approaches will be reviewed in this research. Deep learning methods for automatic segmentation have recently gained popularity since they produce cutting-edge results and are more suited to dealing with this challenge. It is also possible to use deep learning approaches to analyses and evaluate vast quantities of MRI-based image data quickly and objectively. There is a slew of review studies about classic MRI-based approaches for classifying brain tumor pictures. Deep learning approaches were used to classify brain cancers as glioma, meningioma, or pituitary. Conclusions and future advances are also discussed in this section to ensure that MRI-based tumor segmentation methods can be implemented in daily

关键词： Magnetic resonance imaging

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Approximate k-Nearest Neighbor Query over Spatial Data Federation 28th

Approximate k-Nearest Neighbor Query over Spatial Data Fede...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Kaining Tong, Yongxin Shi, Yexuan Zeng, Yuxiang Xu, Yi Chen, Lei Zhou, Zimu Xu, Ke Lv, Weifeng Zheng, Zhiming State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering and Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou China City University of Hong Kong Hong Kong

ISBN: (纸本)9783031306365

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Efficiency of Wireless Communication in Cybersecurity for Data Security 10

Efficiency of Wireless Communication in Cybersecurity for Da...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Shashank, R. Krishna, Konda Hari Takore, Tamirat Tagesse Singla, Atul Veena, Ch Tiwari, Mohit School of Engineering Sr Universtity Department of ECE Telangana Warangal India School of Computing Mohan Babu University Dept. of Cse Tirupati India Wachemo University Department of Electrical and Computer Engineering Ethiopia Ethiopia Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350382471

This Article's aim is to analyse the wireless communication in Cybersecurity. The topic of this paper provides readers with a better grasp of the safety guidelines and measures that are currently on the market, as well as a quick review of the advantages and disadvantages of each kind of security approach. The potential of the system to manage, protect, and distribute confidential data is determined by protection in the world of computers. © 2023 IEEE.

关键词： Cybersecurity data security wireless communication

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Dependence of thermodynamic quantities at freeze-out on pseudorapidity and collision energy in p-p collisions at LHC energies

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Chinese Physics C 2024年第10期48卷 235-246页

作者： Murad Badshah Yahia A.H.Obaidat Haifa I.Alrebdi M.Waqas Muhammad Ajaz Refka Ghodhbani Department of Physics Abdul Wali Khan University MardanMardan 23200Pakistan Department of Physics College of ScienceUniversity of BishaBisha 61922Saudi Arabia Department of Physics College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia School of Mathematics Physics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyan 442002China Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafhaSaudi Arabia

The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies(√s)of 0.9 TeV and 2.36 TeV,as measured by the CMS detector at the Large Hadron Collider(LHC),have been analyzed within various pseudorapidity classes utilizing the thermodynamically consistent Tsallis *** fitting procedure resulted in the key parameters,namely,effective temperature(T),non-extensivity parameter(q),and kinetic freezeout volume(V).Additionally,the mean transverse momentum()and initial temperature(T_(i))of the particle source are determined through the fit function and string percolation method,*** alternative method is employed to calculate the kinetic freezeout temperature(T_(0))and transverse flow velocity(β_(T))from ***,thermodynamic quantities at the freezeout,including energy density(ε),particle density(n),entropy density(s),pressure(P),and squared speed of sound(C_(s)^(2)),are computed using the extracted T and *** is also observed that,with a decrease in pseudorapidity,all thermodynamic quantities except V and q *** trend is attributed to greater energy transfer along the mid pseudorapidity.q increases towards higher values of pseudorapidity,indicating that particles close to the beam axis are far from ***,V remains nearly independent of *** excitation function of these parameters(q)shows a direct(inverse)correlation with collision *** ε,n,s,and P show a strong dependence on collision energies at low *** verification of the thermodynamic inequality ε≥3P suggests the formation of a highly dense droplet-like Quark-Gluon Plasma(QGP).Additionally,the inequality T_(i)>T>T_(0)is explicitly confirmed,aligning with the evolution of the produced fireball.

关键词： charged hadrons tsallis statistics effective temperature non-extensivity parameter pseudorapidity kinetic freeze-out stage multiple freeze-out scenario QGP QCD matter squared speed of sound

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Network Intrusion Detection System Using Optuna

Network Intrusion Detection System Using Optuna

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Nishank Parekh Arzob Sen P. Rajasekaran J. D. Dorathi Jayaseeli P. Robert Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the

关键词： Radio frequency Industries Adaptation models Accuracy Computational modeling Network intrusion detection Telecommunication traffic Real-time systems Tuning Optimization

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A user interface to communicate interpretable AI decisions to radiologists

A user interface to communicate interpretable AI decisions t...

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Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

作者： Ou, Yanchen Jessie Barnett, Alina Jade Mitra, Anika Schwartz, Fides Regina Chen, Chaofan Grimm, Lars Lo, Joseph Y. Rudin, Cynthia Department of Computer Science Duke University Durham United States Department of Radiology Duke University Durham United States School of Computing and Information Science University of Maine Orono United States Department of Biomedical Engineering Duke University Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

ISBN: (数字)9781510660403

ISBN: (纸本)9781510660397

Tools for computer-aided diagnosis based on deep learning have become increasingly important in the medical field. Such tools can be useful, but require effective communication of their decision-making process in order to safely and meaningfully guide clinical decisions. Inherently interpretable models provide an explanation for each decision that matches their internal decision-making process. We present a user interface that incorporates the Interpretable AI Algorithm for Breast Lesions (IAIA-BL) model, which interpretably predicts both mass margin and malignancy for breast lesions. The user interface displays the most relevant aspects of the model's explanation including the predicted margin value, the AI confidence in the prediction, and the two most highly activated prototypes for each case. In addition, this user interface includes full-field and cropped images of the region of interest, as well as a questionnaire suitable for a reader study. Our preliminary results indicate that the model increases the readers' confidence and accuracy in their decisions on margin and malignancy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Risk assessment

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Pan-spatial Earth information system:A new methodology for cognizing the earth system

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The Innovation 2025年第3期6卷 7-8页

作者： Liufeng Tao Yongjun Xu Kang He Xiaochuan Ma Lizhe Wang School of Computer Science China University of GeosciencesWuhan 430074China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China The Key Laboratory of Geological Survey and Evaluation Ministry of EducationChina University of GeosciencesWuhan 430074China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China

Humanity’s understanding of Earth has been a journey of continuous exploration and deepening *** response to the increasingly prominent global environmental issues arising from human activity,Anthropocene science has emerged as an essential new frontier in Earth sciences,following the foundational theories of plate tectonics,global change,and Earth system science.1 In addition to this development,the rise of generative AI technology,exemplified by NVIDIA’s Earth-2 digital twin,heralds a new era of AI-driven scientific research.

关键词： anthropocene science generative ai technology earth sciencesfollowing pan spatial earth information system plate tectonicsglobal earth digital twin understanding earth global environmental issues

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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computer Systems science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Designing Uncorrelated Address Constrain for DNA Storage by DMVO Algorithm

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第2期19卷 866-877页

作者： Cao, Ben Ii, Xue Zhang, Xiaokang Wang, Bin Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Ministry of Education School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

At present, huge amounts of data are being produced every second, a situation that will gradually overwhelm current storage technology. DNA is a storage medium that features high storage density and long-term stability and is now considered to be a feasible storage solution. Errors are easily made during the sequencing and synthesis of DNA, however. In order to reduce the error rate, novel uncorrelated address constrain are reported, and a Damping Multi-Verse Optimizer (DMVO)algorithm is proposed to construct a set of DNA coding, which is used as the non-payload. The DMVO algorithm exchanges objects through black/white holes in order to achieve a stable state and adds damping factors as disturbances. Compared with previous work, the coding set obtained by the DMVO algorithm is larger in size and of higher quality. The results of this study reveal that the size of the DNA storage coding set obtained by the DMVO algorithm increased by 4-16 percent, and the variance of the melting temperature decreased by 3-18 percent. © 2004-2012 IEEE.

关键词： Damping

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