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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Poongodi, S. Sasirekha, K. Rini, Sheela Jeyapriya, S. Rashmi, G. Dona Department of Data Science PSGR Krishnammal College for Women Coimbatore India Department of Computer Science(Artificial Intelligence and Data Science) Dr.SNS Rajalakshmi College of Arts and Science Coimbatore India Deparrtment of Computer Science(PG) PSGR Krishnammal College for Women Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Artificial Intelligence and Machine Learning Kongunadu Arts and Science College Coimbatore India

ISBN: (纸本)9798331512248

Chronic renal disease is a medical disorder in which the kidneys gradually slow down, reducing their ability to filter blood. Diabetes, high blood pressure, heart disease, and a family history of renal failure are the primary risk factors for this condition. The prognosis for patients with chronic kidney disease (CKD) varies according to the stage at which they are diagnosed. Stages 0 and 1 represent the early stages of the disease, whereas stage 4 represents the severe stages, where the kidneys are nearly at the point of failure. Early diagnosis is therefore essential for the patient's survival and the appropriate therapies. Blood tests, biopsies, urine tests, and imaging tests are among the medical diagnostic techniques that can be used to diagnose chronic kidney disease (CKD). However, these medical diagnostic techniques have certain drawbacks and restrictions. They are employed to identify diseases in their later stages and can occasionally yield erroneous results;they are also expensive. Consequently, the researcher has discovered a novel technique for the early identification of CKD that use machine learning algorithms to overcome these complications because machine learning algorithms are becoming increasingly significant in healthcare datasets. These methods also have issues with overfitting, class imbalance, biased results, problems with dataset interpretability, and high computing costs. Therefore, improved preprocessing techniques and machine learning algorithms are used in this research work to overcome the inadequacies of the existing approaches for CKD prediction. The model is tested using the Kaggle dataset on chronic renal disease. Class imbalance and missing attribute values are issues with this dataset. Additionally, this dataset has several categorical attribute values and additional variances in attribute values. A few preprocessing techniques are used to improve the data quality, including K-Nearest Neighbour for filling in missing attribute val

关键词： Chronic Kidney Disease ELM K-NN PSO SMOTE

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Asymmetric Self-Play for Learning Robust Human-Robot Interaction on Crowd Navigation Tasks 3

Asymmetric Self-Play for Learning Robust Human-Robot Interac...

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3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Ma, Jun Qiu, Quecheng Chen, Shuyan Yao, Shunyi Chen, Yuan Ji, Jianmin School of Data Science China USTC School of Computer Science and Technology China

ISBN: (纸本)9798350331974

A robot can make a sound to aware nearby pedestrians during its navigation, which often results in a more efficient and safer trajectory in a crowded environment. However, it is challenging to integrate such interaction capability into an existing robot navigation policy. In this paper, we propose a deep reinforcement learning (DRL) approach for integration, which results in effective interactive robot navigation policies in crowded environments. In particular, we specify the interaction capability by a set of high-level actions with flexible control. Then the navigation policy can be considered as a specific implementation of the 'move' action. Based on these high-level actions, we can train a robust human-robot interaction policy via asymmetric self-play, where the robot and some pedestrians, considered as naughty kids, play a game. Then the interaction policy can not only interact with pedestrians who cooperate with the robot but also with pedestrians who try to block the robot. We evaluate our approach in various simulation environments and compare it with multiple approaches. The experimental results show that our approach is robust and performs well in dense environments that are challenging for others. We also deploy the trained policy on a robot and evaluate its performance in multiple real-world crowded environments. A demonstration video is available online at https://***/x32YmivsIh0. © 2022 IEEE.

关键词： Reinforcement learning

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SG-NeRF:Sparse-Input Generalized Neural Radiance Fields for Novel View Synthesis

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Journal of computer science & Technology 2024年第4期39卷 785-797页

作者： Kuo Xu Jie Li Zhen-Qiang Li Yang-Jie Cao School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 450002China Intelligent Big Data System(iBDSys)Lab Shanghai Jiao Tong UniversityShanghai 200240China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China

Traditional neural radiance fields for rendering novel views require intensive input images and pre-scene optimization,which limits their practical *** propose a generalization method to infer scenes from input images and perform high-quality rendering without pre-scene optimization named SG-NeRF(Sparse-Input Generalized Neural Radiance Fields).Firstly,we construct an improved multi-view stereo structure based on the convolutional attention and multi-level fusion mechanism to obtain the geometric features and appearance features of the scene from the sparse input images,and then these features are aggregated by multi-head attention as the input of the neural radiance *** strategy of utilizing neural radiance fields to decode scene features instead of mapping positions and orientations enables our method to perform cross-scene training as well as inference,thus enabling neural radiance fields to generalize for novel view synthesis on unseen *** tested the generalization ability on DTU dataset,and our PSNR(peak signal-to-noise ratio)improved by 3.14 compared with the baseline method under the same input *** addition,if the scene has dense input views available,the average PSNR can be improved by 1.04 through further refinement training in a short time,and a higher quality rendering effect can be obtained.

关键词： Neural Radiance Fields(NeRF) multi-view stereo(MVS) new view synthesis(NVS)

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language

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SN computer science 2023年第4期4卷 354页

作者： Mahanty, Mohan Vamsi, Bandi Madhavi, Dasari Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Sridevi Women’s Engineering College Hyderabad 500075 India

There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary goal of this work is to propose an auto-encoder–decoder-based deep neural network for translating English to the Telugu language. Deep neural networks are used for learning, where input language is supplied to the neural network following data processing and analysis. The neural network then does auto-tuning, which helps to improve the translation quality. This proposed work produces target sentences in the Telugu language, resulting in a higher Bilingual Evaluation Understudy (BLEU) Score and a lower Word Error Rate (WER). The BLEU finds the similarity of the translated text by Machine Translation models with high-quality references and WER finds better accuracy in identifying the target sentence. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep neural network Encoder-and-decoder English-to-Telugu Neural machine translation

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Improved Meet-in-the-Middle Cryptanalysis on Kupyna 13

Improved Meet-in-the-Middle Cryptanalysis on Kupyna

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13th International Conference on Communication and Network Security, ICCNS 2023

作者： Xu, Jinxing Zou, Jian Lan, Qiufu Li, Jiayin College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

Kupyna is an AES-like hash function that has been confirmed as the Ukrainian hash standard DSTU 7564:2014 in 2015. In this paper, we present some preimage attacks and collision attacks on Kupyna. We combine guess-and-determine and MITM methods, using a MILP-based MITM attack tool, to apply them to the Kupyna hash cryptographic algorithm. We improve the preimage attack on 6-round Kupyna-256 (OT), increasing the complexity from the previous best attack complexity of 2240 to 2224. Additionally, we also propose, for the first time, collision attacks on 6-round Kupyna-256 (OT) and 8-round Kupyna-512 (OT), with complexities of 2116 and 2444 respectively. © 2023 ACM.

关键词： Hash functions

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Real-Time Traffic Analysis Using Vehicle Trajectory Similarity in Edge Computing

Real-Time Traffic Analysis Using Vehicle Trajectory Similari...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Jang, Jae-Geun Jung, Jin-Uk Kim, Seonhyeong Choi, Ayoung Kwon, Young-Woo Graduate School of Data Science Kyungpook National University Daegu Korea Republic of School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9798331507022

The majority of road accidents occur as a result of driver irresponsibility. Utilizing deep learning models to analyze traffic CCTV can effectively mitigate traffic accidents. This paper aims to identify and monitor vehicles and assess traffic conditions by comparing and categorizing the similarities between vehicle trajectories. Three similarity measurement methods are employed: cosine similarity, Jensen-Shannon divergence, and Euclidean distance similarity. The results show that lanes and traffic patterns can be effectively identified using the presented approach. Through our approach, one can further develop traffic monitoring and analysis advancements, thereby enhancing road safety and traffic management © 2025 IEEE.

关键词： Highway traffic control

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Implementing Hadoop and Moodle Integrated Student Support System 5

Implementing Hadoop and Moodle Integrated Student Support Sy...

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5th International Conference on Advanced Information Technologies, ICAIT 2024

作者： Aung, Myint Myat Min Htet, Shin Set, Hein Myo Hpone Thant, Aung Htut, Sithu Htet, Kyaw Zin Nwe Myint Thein, Nwe University of Information Technology Data Distribution Management Lab Yangon Myanmar University of Information Technology Data Distribution Management Lab Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Learning management systems (LMS) generate large amounts of real-time data which has the potential to provide valuable insights. Hadoop MapReduce is a scalable framework that is designed to handle such data. This paper presents a system architecture that integrates Hadoop MapReduce into Moodle, a popular LMS, for analyzing academic data. The implementation of the architecture allows users to generate a variety of reports from student attendance logs through a Moodle plugin which utilizes the Hadoop cluster for processing data. Users such as lecturers can then utilize these reports to identify which students do and do not engage in which courses as well as monitor their eligibility for exams. The use of Hadoop MapReduce provides high performance computing (HPC) capabilities that offer distributed parallel processing and economical scalability by means of adding commodity nodes to the cluster. © 2024 IEEE.

关键词： MapReduce

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LifeSaver: Predictive Load Limit Estimation for Transport Vehicles in Hilly Areas

LifeSaver: Predictive Load Limit Estimation for Transport Ve...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Chopra, Vaibhav Rao, Chanakya Soni, Moksh Mishra, Prashant Plaksha University Computer Science & Artificial Intelligence Mohali India Plaksha University Data Science Economics & Business Mohali India Plaksha University Robotics & Cyber-Physical Systems Mohali India

ISBN: (纸本)9798331529833

The transportation of essential goods in mountainous regions faces severe logistical challenges and frequent disruptions. To mitigate these difficulties, transport companies often overload trucks, which, though cost-saving, significantly heightens the risk of accidents and mechanical failures. This paper presents the development of a device that detects overloaded and insecurely fastened loads on trucks and commercial vehicles. Using advanced load sensors, the device offers real-time monitoring of cargo weight distribution, alerting drivers and authorities to unsafe conditions. The initial prototype utilised two basic load cells and an Arduino microcontroller. The second version was enhanced with four load cells and extended sensors. This version was tested by placing an electric golf cart onto the prototype. Various loads were then added to the cart in different orientations to assess whether the system could accurately detect improper or excessive load conditions. © 2025 IEEE.

关键词： Trucks

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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