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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： El Aziz, Mahmoud I. Abd Elshewey, Ahmed M. Tarek, Zahraa El-Kenawy, El-Sayed M. Ghazy, Ahmed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Education Department of Curriculum Instruction and Educational Technology Kafrelsheikh33511 Egypt Suez University Faculty of Computers and Information Computer Science Department Suez Egypt Mansoura University Faculty of Computers and Information Computer Science Department Mansoura35561 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India Kafrelsheiku University Faculty of Artificial Intelligence Dept. of Machine Learning 33516 Egypt

ISBN: (纸本)9798350387063

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on machine learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively. © 2024 IEEE.

关键词： Prediction models

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The Internal State of an LLM Knows When It's Lying

arXiv

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arXiv 2023年

作者： Azaria, Amos Mitchell, Tom School of Computer Science Ariel University Israel Machine Learning Dept. Carnegie Mellon University PittsburghPA United States

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM's internal state can be used to reveal the truthfulness of statements. This includes both statements provided to the LLM, and statements that the LLM itself generates. Our approach is to train a classifier that outputs the probability that a statement is truthful, based on the hidden layer activations of the LLM as it reads or generates the statement. Experiments demonstrate that given a set of test sentences, of which half are true and half false, our trained classifier achieves an average of 71% to 83% accuracy labeling which sentences are true versus false, depending on the LLM base model. Furthermore, we explore the relationship between our classifier's performance and approaches based on the probability assigned to the sentence by the LLM. We show that while LLM-assigned sentence probability is related to sentence truthfulness, this probability is also dependent on sentence length and the frequencies of words in the sentence, resulting in our trained classifier providing a more reliable approach to detecting truthfulness, highlighting its potential to enhance the reliability of LLM-generated content and its practical applicability in real-world scenarios. © 2023, CC BY-NC-ND.

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A Fast and Scalable Method for Inferring Phylogenetic Networks from Trees by Aligning Lineage Taxon Strings

arXiv

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arXiv 2023年

作者： Zhang, Louxin Abhari, Niloufar Colijn, Caroline Wu, Yufeng Dept. of Mathematics Centre for Data Science Machine Learning National University of Singapore 119076 Singapore Dept. of Mathematics Simon Fraser University BurnabyBCV5A 1S6 Canada Dept. of Computer Science and Engineering University of Connecticut StorrsCT06269 United States

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every non-leaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on *** Codes 05C30 © 2023, CC BY.

关键词： Phylogenetic

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Multi-View Brain Network Analysis with Cross-View Missing Network Generation

Multi-View Brain Network Analysis with Cross-View Missing Ne...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Luo, Gongxu Li, Chenyang Cui, Hejie Sun, Lichao He, Lifang Yang, Carl MBZUAI Department of Machine Learning Abu Dhabi United Arab Emirates University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada Emory University Department of Computer Science AtlantaGA United States Lehigh University Dept. of Computer Science and Engineering BethlehemPA United States

ISBN: (纸本)9781665468190

Parkinson's Disease (PD), one of the most common neurological disorders, has long been a challenge in public health clinical diagnosis as well as scientific understanding. Recently, there has been an upsurge of interest in brain network analysis which benefits the understanding of brain functions and early detection of neurological disorders extensively. Multi-view brain networks with different connectivity patterns among regions of interests (ROIs) can be constructed to reflect different and complementary perspectives of the brain connectivity profile. However, the extraction of such multi-view brain networks relies on the availability of multiple neuroimaging modalities and heavy data preprocessing, which often leads to severe missing data in either view. The cross-view missing issue hinders the pragmaticality of multi-view representation learning and downstream analysis. In this work, we formulate the novel problem of cross-view brain network generation and propose CroGen, a graph generative model that can generate the missing view when only one view is given. Specifically, GroGen leverages the potential correlation between diverse views of brain networks of the same individuals. Moreover, we design a pre-train schema to make full use of the labeled individuals with only single views of brain networks. Extensive experiments on real-life Parkinson's Progression Markers Initiative (PPMI) cohort demonstrate the supreme effectiveness of CroGen over baselines on both cross-view generation tasks and downstream PD classification. © 2022 IEEE.

关键词： Neuroimaging

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Smart Emergency Management System Using Raspberry Pi Pico, GPS and GSM Modules 4

Smart Emergency Management System Using Raspberry Pi Pico, G...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Soumya, E. Prakash, Sargu Chandra Priyanka, Ms. P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India St. Martin's Engineeringollege Dept. of Artificial Intelligence and Machine Learning Telangana Dhulapally Secunderabad India St. Martin's Engineeringollege Deptarment of Cse Telangana Dhulapally Secunderabad India Telangana Dhulapally Secunderabad India

ISBN: (纸本)9798331523923

This paper proposes a GSM-based children's rescue system designed for earthquake emergencies, emphasizing cost-effectiveness and simplicity while ensuring rapid child rescue. The system operates in three key stages: data acquisition, classification, and alert notification. Sensors collect temperature, pH, and heart rate data, which are then digitized and classified to assess the child's condition. If abnormal values are detected, an alert notification is sent via GSM to parents and rescue teams, providing real-time location data through GPS. The hardware implementation, based on Raspberry Pi, converts analog sensor signals into digital data for processing. The system's efficacy is validated through various scenarios, demonstrating its ability to detect critical conditions and promptly trigger alerts, enhancing child safety during disasters. © 2025 IEEE.

关键词： Earthquakes

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Edge Computing Devices Authentication using Quantum Computing

Edge Computing Devices Authentication using Quantum Computin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vinayak A. Telsang Mahabaleshwar S. Kakkasageri Anil D. Devangavi Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(V V), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices.

关键词： Privacy Quantum computing Costs Authentication Vehicular ad hoc networks Blockchains Registers Servers Industrial Internet of Things Edge computing

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Mallak H. Al-shamaileh Aser M Matarneh Omar R. Daoud Mohammad Al-Khawaldah I Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Data Science and Machine Learning Dept. Pioneers International Academy Amman Jordan Communications and Computer Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features.

关键词： Wireless communication Correlation coefficient Simulation Bandwidth Specific absorption rate Impedance MIMO communication

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SSL-VoxPart: A Novel Solid-State LiDAR-Tailored Voxel Partition Approach for 3D Perception

SSL-VoxPart: A Novel Solid-State LiDAR-Tailored Voxel Partit...

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IEEE SENSORS

作者： Nico Leuze Henry Schaub Maximilian Hoh Alfred Schoettl Dept. of Electrical Engineering and Information Technology Institute for Applications of Machine Learning and Intelligent Systems University of Applied Science Munich Munich Germany

LiDAR-based 3D point cloud segmentation is a crucial component in the perception system of many applications that rely on a thorough 3D environment understanding. LiDAR point clouds are unstructured, sparsely scattered in 3D space and have non-uniform densities, which restricts the use of ordinary convolutional neural networks. Tackling the point clouds irregular format, several approaches are introduced that divide the 3D space into a discretised volumetric grid. However, voxelization inevitably abandons the 3D topology of point clouds and suffers from information loss. In this paper, we propose SSL-VoxPart, a novel approach of scan-pattern related partitioning for voxel-wise semantic segmentation suitable to MEMS-actuated Solid-State LiDARs. We argue scan-pattern related voxel partitioning is worth being considered by showing improvements in extracting semantic information for voxel-wise point cloud segmentation, while reducing lossy cell-label encoding. We specifically conduct to the problems of crowded scene analysis. Crowds represent locally high density distributions of people with heavy occlusion effects, demanding for fine-grained predictions. We introduce a SSL data generation pipeline tailored to surveillance scenarios and validate our proposed method on the resulting dataset.

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Probabilistic Fusion of dept. Maps with Local Variance Estimation

Probabilistic Fusion of Depth Maps with Local Variance Estim...

引用

IEEE SENSORS

作者： Henry Schaub Nico Leuze Maximilian Hoh Alfred Schoettl Dept. of Electrical Engineering and Information Technology Institute for Applications of Machine Learning and Intelligent Systems University of Applied Science Munich Munich Germany

dept. sensors in general, but especially consumer level sensors, show significantly varying noise characteristics depending on the environment and the measured material. If only an empirically determined sensor model is used, the quality of some reconstructed surface areas might therefore be worse than estimated. In order to gain an accurate surface approximation with meaningful corresponding estimation variances, noise characteristics of surface elements must be treated individually. We propose a probabilistic approach that combines an empirically determined sensor noise model with an incrementally updated estimation of the local noise distribution. Experiments on a publicly available dataset demonstrate that our algorithm reconstructs challenging scenes with comparatively high accuracy and fewer outliers.

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Rainfall Prediction using Novel Approach for Enhancing Crop Yield 2

Rainfall Prediction using Novel Approach for Enhancing Crop ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Seelam, Visweswararao Vikruthi, Sriharsha Bhagavatham, Naresh Kumar Nagendrudu, P.V.V.S.D. Srinivasarao, Goda Chakravarthi M, Ayyappa PACE Institute of Technology and Sciences Dept of CSE India B V Raju Institute of Technology Dept of CSE Telangana Narsapur Medak502313 India Vignana bharathi Institute of Technology Dept of CSE Hyderabad India Aditya University Department of Artificial Intelligence and Machine Learning Surampalem India KL University Vaddeswaram Dept of CSE Andhra Pradesh Guntur India KKR & KSR Institute of Technology & Sciences Dept of CSE-Data Science Andhra Pradesh Guntur India

ISBN: (纸本)9798331519056

In India, agriculture is one of the fastest-growing fields to improve the country's economy. Based on several factors like climatic conditions, predicting rainfall in these stages is one of the most significant tasks. In recent years, unexpected and unpredictable weather conditions have been observed in some places. This study mainly focused on developing the Hybrid algorithm (HA) to predict rainfall in the early stages and find accurate weather conditions. The proposed approach also called as Novel Approach that improves the algorithm's strength by adopting the advanced preprocessing and feature extraction technique. The training model random forest (RF) also used to find the accurate patterns from the given dataset samples. The preprocessing technique focused on cleaning and transforming the raw data to remove the noise from the input dataset, improving the data quality. The proposed approach combines Autoencoders and Linear Regression (LR) to accurately predict rainfall. Extensive experiments are carried out to evaluate the performance of the hybrid algorithm using historical meteorological data from multiple regions with varying climatic conditions. The results show that the hybrid algorithm outperforms traditional methods and individual machine-learning models in terms of accuracy, robustness, and generalization. © 2024 IEEE.

关键词： Adversarial machine learning

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