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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Shyamala Gowri, B. Nair, S.Anu H Sanal Kumar, K.P. Kamalakkannan, S. Annamalai University Department of Computer Science and Engineering India Annamalai University Department of CSE India R v Government Arts College Chengalpattu Department of CS India School of Computing Sciences VISTAS Department of Information Technology India

ISBN: (纸本)9798350340600

Cancers are the most disastrous and inevitable ailment that occurs in individuals. Due to the hazardous effects of cancer, people get at death in very early age. In today's date, cancer is categorized into many types, which are affected by the external and internal parts of the body. In general, cancers are caused by the growth of abnormal tissues where cancer originates and it is gradually spread to other parts. Therefore, the medical industry struggles to detect the different types of cancer disorders without any loss of people. Hence, the automated detection system is implemented to predict cancer in its early stages to prevent the people gets worsening. Normally, the collection of individual data is another challenging concern. Several methods have been implemented yet they exist with constraints to provide better results. Machine learning models are also used, but it does not tackle the big data collection process and also fail to obtain the relevant features. Henceforth, the deep learning model has emerged for various processes like prediction, classification, and recognition. So, a new and improved classification framework for classifying cancer is executed in this paper. At first, the data is gathered from the benchmark database. From the data, the genes are optimally selected using an Improved Arithmetic Optimization Algorithm (IAOA). Then, the optimally chosen genes are given as input to the 'Optimized Deep Neural Network (ODNN)' for classification. The constraints in the DNN framework are optimized by the improved AOA. From the DNN, the classified output is obtained. Various experimentations are carried out by contrasting the developed optimization algorithm enhanced DNN model to verify the efficient working of the suggested cancer classification model. Throughout the result analysis, the accuracy and precision rate of the designed method is 93.42% and 9363% for all datasets. © 2023 IEEE.

关键词： Deep neural networks

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Internet of things (IoT) applications and Challenges: A Study 7

Internet of things (IoT) applications and Challenges: A Stud...

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7th International Conference on computing, Communication and Security, ICCCS 2022 and 2022 4th International Conference on Big Data and Computational Intelligence, ICBDCI 2022

作者： Kumar, Rajesh Anandaram, Harishchander Joshi, Kapil Kumar, Vivek Reshi, Jadeep Saini, Rakesh Kumar Meerut Institute of Technology Department of CSE Meerut India Amrita School of Engineering Coimbatore Amrita Vishwa Centre for Computational Engineering and Networking Vidyapeetham India Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India DIT University School of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350309904

Internet of Things (IoT) transformed finished the general unlimited wrapper of persons, keen devices, intelligent effects, statistics, and arguments of attention. It is no unidentified that as continually accomplishes important additional to the network, the experiments of protective the archives that they inform and the transferences that they effort have acquired progressively heedful. Through the centuries, we've obvious about pouring in Internet of Things (IoT) devices, expansively in two domains-in homes and in accumulating. Since those are balanced and shut structures, the responsibilities of protective the devices regulator with the performance residence whole salers. In this chapter, we will state-owned arithmetic nonviolent protection in hypothesis and supplementary governments. Administrations which contribute current, oil and petrol, riddling, treatment drugs, eating times and imbuement, water cure, and a lot more are constantly bidding to comprise the accurate covers of protection, as they permit on increasingly more device and devices on the network. Apparatus discoverers and bloom crown movements administrators constantly expressions exercise to viewpoint their bodily things from numerical hazards. It is also for every one of these activities, the background of the data, topologies of IoT applications, and problems of vulnerability controller and hopeful consistence worldly normally. © 2022 IEEE.

关键词： Internet of things

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN Computer Science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN Computer vision Deep learning Smart glasses Smoke monitoring

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SAC-ViT: Semantic-Aware Clustering Vision Transformer with Early Exit

arXiv

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arXiv 2025年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Wei, Dawei Li, Zhijun Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in global modeling but faces deployment challenges on resource-constrained devices due to the quadratic computational complexity of its attention mechanism. To address this, we propose the Semantic-Aware Clustering Vision Transformer (SAC-ViT), a non-iterative approach to enhance ViT’s computational efficiency. SAC-ViT operates in two stages: Early Exit (EE) and Semantic-Aware Clustering (SAC). In the EE stage, downsampled input images are processed to extract global semantic information and generate initial inference results. If these results do not meet the EE termination criteria, the information is clustered into target and non-target tokens. In the SAC stage, target tokens are mapped back to the original image, cropped, and embedded. These target tokens are then combined with reused non-target tokens from the EE stage, and the attention mechanism is applied within each cluster. This two-stage design, with end-to-end optimization, reduces spatial redundancy and enhances computational efficiency, significantly boosting overall ViT performance. Extensive experiments demonstrate the efficacy of SAC-ViT, reducing 62% of the FLOPs of DeiT and achieving 1.98× throughput without compromising performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Continuous Prompt for Chemical Language Model Aided Anticancer Synergistic Drug Combination Prediction

Continuous Prompt for Chemical Language Model Aided Anticanc...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Geng, Guannan Zhao, Lingling Wang, Chunyu Liu, Xiaoyan Wang, Junjie The First Affiliated Hospital of Harbin Medical University Department of Endocrinology Harbin China Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350324457

Identifying synergistic drug combinations is paramount significance in addressing complex diseases while reducing the risks of toxicities and other adverse effects. Although a plethora of computational methods have been proposed in this domain, most of them are underpinned only by physicochemical or biological features. Recently, Chemical Language Models (CLMs) are shown to be capable of learning better representations that hold utility across diverse tasks, from molecular property prediction, de novo drug design, drug-target interaction, and more. In this study, we proposed CLMSyn, a continuous prompt for CLM aided synergistic drug combinations prediction. Unlike existing works employ CLMs for downstream tasks, we adopt the prompt learning to fine-tune CLM, that is, only train small-scale prompt while keeping CLM fixed. Furthermore, we harness the the multi-head attention mechanism to fuse the learned vector from the CLM, chemical descriptors and gene expression of cell line. A comprehensive array of experiments conducted on a benchmark dataset, encompassing four distinct synergy types, substantiates the superior performance of CLMSyn when contrasted against existing state-of-the-art methods. These empirical findings provide compelling evidence attesting to the efficacy of CLMSyn as a potent instrumentality in expediting the identification of pioneering combination therapies. © 2023 IEEE.

关键词： Chemical Language Model Drug Combination Multi-head Attention Prompt Learning

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Customizable DDoS Attack Data Generation in SDN Environments for Enhanced Machine Learning Detection Models 5

Customizable DDoS Attack Data Generation in SDN Environments...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Gayantha, Nadeera Rajapakse, Chathura Senanayake, Janaka University of Kelaniya Dept. of Industrial Management Sri Lanka Robert Gordon University School of Computing Engineering and Technology Aberdeen United Kingdom

ISBN: (纸本)9798331530983

Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of Software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such as KDDCup '99 and CICIDS 2017, are either outdated or fail to capture SDN-specific characteristics, limiting their effectiveness in detecting modern DDoS attacks. This paper proposes a framework for generating a comprehensive, SDN-specific dataset using a virtual environment that integrates Mininet, the Ryu controller, and Python-based automation. The dataset incorporates advanced flow-level metrics, including SYN counts, queue lengths, and real-time traffic dynamics, reflecting contemporary attack scenarios such as ICMP floods, TCP SYN floods, and UDP floods. By addressing the limitations of traditional datasets, this custom dataset enhances ML model training for DDoS detection in SDN environments, providing improved accuracy and adaptability. Contributions include a scalable SDN-based dataset generation framework, enriched feature sets for ML training, and a comprehensive approach to capturing both legitimate and malicious traffic dynamics. This study highlights the potential of SDN programmability in advancing security research and offers a robust tool for the development of reliable DDoS detection mechanisms. © 2025 IEEE.

关键词： Software reliability

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DevFlair: A Framework to Automate the Pre-screening Process of Software engineering Job Candidates 4

DevFlair: A Framework to Automate the Pre-screening Process ...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Jayasekara, R.T.R. Kariyawasam, K.G.S.S.K. Jayasinghe, S.L. Kudarachchi, K.A.N.D. Rajapaksha, Dilini Thelijjagoda, Samantha Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia SLIIT Business School Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The HR department of a technology company receives hundreds of job applications for each Software engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose "DevFlair", a framework to automate pre-screening Software engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits. © 2022 IEEE.

关键词： Machine learning

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Evaluating The Mixing Performance in A Planar Passive Micromixer With T-shape and SAR Mixing Chambers: Comparative Study 2

Evaluating The Mixing Performance in A Planar Passive Microm...

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

作者： Ababneh, Jafar Deif, Mohanad A. Solyman, Ahmad Zekry, Rahma S. Hafez, Mohamed Sharfo, Samer M. Cyber Security Zarqa University The Faculty of Information Technology Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Artificial Intelligence 12566 Egypt School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom University Department of Bioelectronics Cairo Egypt INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia

ISBN: (纸本)9798350373363

In Microfluidic devices have gained significant interest in various fields, including biomedical diagnostics, environmental preservation, animal epidemic avoidance, and food safety regulation. Micromixing phenomena are crucial for these devices' functionality, as they accurately and efficiently manipulate fluids within microchannels. The process aims to blend samples accurately and swiftly within these scaled-down devices, governed by the promotion of dispersion among distinct fluid species or particles. Advancements in passive and active micromixers have led to innovative designs incorporating diverse processes to enhance mixing efficiency. Examples include two-dimensional impediments, controlled imbalanced collisions, and complex configurations like spiral and convergence-divergence structures. Active micromixers use external cues to initiate and regulate mixing processes, including thermal, magnetic, sound, pressure, and electrical fields. The trajectory of micromixing technologies is significantly influenced by current developments in microfluidics. One notable advancement is the incorporation of micromixers into 3D printing methodologies, facilitating the development of adaptable microfluidic systems. Additionally, the incorporation of microfluidic principles into paper-based channels creates opportunities for the development of cost-effective and portable diagnostic devices. The process of micro-mixing is critical in boosting the functionalities of these devices. © 2023 IEEE.

关键词： Microfluidics

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Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning

arXiv

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arXiv 2024年

作者： Xiao, Haowen Liu, Guanghui Gao, Xinyi Li, Yang Lv, Fengmao Chu, Jielei School of Information Science and Technology Southwest Jiaotong University China School of Computing and Artificial Intelligence Southwest Jiaotong University China College Of Computer Science And Technology Harbin Engineering University China

Self-supervised learning (SSL) has shown remarkable data representation capabilities across a wide range of datasets. However, when applied to real-world datasets with long-tailed distributions, performance on multiple downstream tasks degrades significantly. Recently, the community has begun to focus more on self-supervised long-tailed learning. Some works attempt to transfer temperature mechanisms to self-supervised learning or use category-space uniformity constraints to balance the representation of different categories in the embedding space to fight against long-tail distributions. However, most of these approaches focus on the joint optimization of all samples in the dataset or on constraining the category distribution, with little attention given to whether each individual sample is optimally guided during training. To address this issue, we propose Temperature Auxiliary Sample-level Encouragement (TASE). We introduce pseudo-labels into self-supervised long-tailed learning, utilizing pseudo-label information to drive a dynamic temperature and re-weighting strategy. Specifically, We assign an optimal temperature parameter to each sample. Additionally, we analyze the lack of quantity awareness in the temperature parameter and use re-weighting to compensate for this deficiency, thereby achieving optimal training patterns at the sample level. Comprehensive experimental results on six benchmarks across three datasets demonstrate that our method achieves outstanding performance in improving long-tail recognition, while also exhibiting high robustness. © 2024, CC BY.

关键词： Self-supervised learning

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A Machine Learning-based Automatic Model to Predicting Performance of Students

A Machine Learning-based Automatic Model to Predicting Perfo...

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2022 IEEE International Conference on Current Development in engineering and technology, CCET 2022

作者： Garg, Atul Garg, Nidhi Bansal Ghosh, Pinaki Bansal, Ankit Lilhore, Umesh Kumar Simaiya, Sarita Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India School of Advanced Computing Sage University Bhopal India Chandigarh University Department of Computer Science and Engineering Karnataka Mohali India

ISBN: (纸本)9781665454155

Machine learning offers a variety of techniques for use in diverse fields. Massive quantities of student data are produced in educational institutions, and machine-learning techniques are a precious tool for identifying patterns in individuals' behavior. Academicians can learn valuable data concerning the students' expertise and how it relates to their educational assignments by critically examining and evaluating these data. This data serves as the input for cutting-edge techniques and algorithms that can forecast students' academic achievement. This research presents a machine learning-based automatic model for predicting the performance of students. The popular ML methods are Random Forest, K-Neighbors, Extra Trees, Ada-Boost, Decision Tree, Logistic Regression, Gaussian NB, and Bernoulli NB. This research utilizes an online student performance dataset collected from Kaggle. The findings demonstrate that Random forest and decision trees outperform other techniques based on high precision as well as the capacity to interpret the guidelines and structure that were developed for predicting students' educational success. © 2022 IEEE.

关键词： Students

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