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Placement Prediction Using the Artificial Neural network (ANN) 3

Placement Prediction Using the Artificial Neural Network (AN...

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3rd international Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Priyadarsini, Jukta Badiger, Veena S. Sheetal Jacintha, Anitha DSouza Presidency College Department of Computer Applications Bengaluru India Presidency University Presidency College School of Engineering Bengaluru India

ISBN: (纸本)9798331515911

Placements are of utmost significance to academic organizations and college students. A strong foundation for the professional field is built up for the student beforehand, and a positive placement report gives a school or college a competitive edge in the educational marketplace. An automated method of statistical evaluation that streamlines the creation of analytical versions is machine learning. In this study, a computer that predicts whether a student might be located or not is the focus, based solely on the student's credentials, historical data, and experience. The performance is evaluated using students' datasets from presidency college and accuracy achieved for our model is 96%. © 2025 ieee.

关键词： Adversarial machine learning

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AIPenTool: A Unified Approach to Automated Penetration Testing for Enhanced network and Web Application Security 3

AIPenTool: A Unified Approach to Automated Penetration Testi...

引用

3rd international Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Niharika P. Vijay Kumar Gowda, K.K. Dhanushree, M.S. Rvitm Dept. of Ise Bangalore India

ISBN: (纸本)9798331515911

In response to the growing need for effective cybersecurity practices, this paper presents a unified penetration testing tool designed to streamline and automate the vulnerability assessment process for both network and web applications. Existing penetration testing tools often require the use of multiple disparate applications, which complicates workflows, limits functionality, and increases the burden on users. This tool integrates industry-standard tools like Nmap, Metasploit, and OWASP ZAP within a single platform, providing comprehensive vulnerability scanning, real-time monitoring, and customizable reporting in a user-friendly interface. By offering multi-platform support and automated workflows, this solution enhances accessibility for both technical and non-technical users, thereby increasing the efficiency and accuracy of security testing. Testing results demonstrate the tool's effectiveness in reducing false positives and improving the detection rate of common vulnerabilities, underscoring its value for modern cybersecurity practices. Future directions include the integration of AI-driven analysis for further vulnerability detection optimization. © 2025 ieee.

关键词： Websites

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Integration of Traffic Shaping with Time-Sensitive networking for Effective Traffic Management 3

Integration of Traffic Shaping with Time-Sensitive Networkin...

引用

3rd international Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Penumada, Archana Reddy Gundapaneni, Divya Jyothi Chennupati, Akshitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Time-Sensitive networking (TSN) has recently become an important standard to support time-sensitive reliable low-latency data transmission in industrial and real-time applications. This work explores the use of different traffic shaping algorithms, namely, credit-based shaping (CBS) and time-aware shaping (TAS) alongside frame preemption—a TSN instance which gives precedence to high-importance frames by means of lower-priority traffic interruption—to extend TSN’s traffic management capabilities. By controlling data flow and allowing for high priority packets to pass through unimpeded, traffic shaping algorithms serve an essential function in making the network more efficient and reducing congestion rates. The work compares these algorithms in isolation and also in combination over a network model, comprising of four switches and three end users for each switch, using the OMNeT++ simulation environment. This study aims to contribute insights into optimal shaping techniques for TSN, enhancing its suitability for critical network applications. © 2025 ieee.

关键词： Traffic congestion

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Analysis of optimization Techniques for Dynamic Neural networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

引用

3rd international Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 ieee.

关键词： Neural network models

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Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge computing for Smart Cities 3

Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, an...

引用

3rd international Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, G. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

An Internet of Things (IoT) network is made up of multiple connected devices to collect and exchange information over the Internet for remote monitoring and control. IoT has drawbacks such as data security vulnerabilities posed by networking devices, data protection issues associated with data collection and sharing, and interoperability challenges among different IOT platforms and devices. A Global Channel Matching (GCM) algorithm is integrated into the Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge computing for Smart Cities (ECMMS) in this article, which aims to optimize relay transmission links for use in IoT networks. In a smart city environment, the GCM algorithm is designed to mimic Firefly behavior and dynamically adjust relay placement for network efficiency and interference reduction. The network model includes IoT devices connected to a Virtual Mobile Edge computing (MEC) fabric to facilitate task offloading and distributed computing. The proposed framework aims to improve computational efficiency by creating virtual servers that use networked IoT devices based on task requirements. ECMMS has demonstrated through detailed simulations and evaluations that it is more effective in optimizing relay selection, reducing interference, and achieving optimal data rates, which supports the scalability and reliability of IoT applications in smart city infrastructure. Using these parameters with discussed the overcomes of our ECMMS model when compared to MNMD, SHBT, and IHAG models. Such as packet loss, average throughput, average end-to-end delay, energy efficiency, and data success rate where calculate. Here packet loss and data success rates are high rates of 99 % and 95.74 %. Were average throughput, energy efficiency, and average end-to-end delay archives low efficiency of 71.02, 254.06, and 254.06 are calculated. © 2025 ieee.

关键词： Mobile edge computing

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

3rd international Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 ieee.

关键词： Application programs

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Privacy and Security Concerns in Edge-Cloud Systems: An In-Depth Analysis 3

Privacy and Security Concerns in Edge-Cloud Systems: An In-D...

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3rd international Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kaki, Samuel Suhas Kantareddygari, Jagadeswara Reddy Aishwarya, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-time analytics. These systems are highly vulnerable to significant security and privacy challenges, which include the following: data confidentiality and integrity in the process of transmission, secure protection of sensitive data stored on the edge against physical and remote attacks, and maintaining availability under dynamic network conditions. More challenges related to ensuring users' private nature through anonymizing data, obtaining consent for all processes involved, and compliance with specific regulations like GDPR and CCPA. This work incorporates more enhanced cryptographic methods in the transfer of data between heterogeneous systems and environment-based anomaly detection, advanced privacy-preserving analytics to avoid extraction of private data, adaptive control mechanisms in place for Edge-Cloud for minimizing vulnerability with enhanced trust along with the maintenance of data integrity. The key benefits would be robust mitigation of threats, better interoperability between edge and cloud platforms, and alignment with evolving security standards, which promotes wider adoption across various sectors. © 2025 ieee.

关键词： Cloud platforms

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A 3D gesture posture estimation and interaction method 24

A 3D gesture posture estimation and interaction method

引用

3rd international Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Li, Heng Wu, Xinyi Ren, Wenkai Innovation Center for GPU and Intelligent Computing System Wuhan Digital Engineering Institute Hubei Wuhan China

ISBN: (纸本)9798400707308

Aiming the needs of non-contact convenient interaction in applications, we proposed a 3D gesture pose estimation and interaction feedback method. The accuracy of gesture posture estimation is improved by a cascade gesture posture estimation network based on DeepPrior++ and the Kalman filter. We also design a 3D gesture interaction modal. The recognized gestures and estimated gesture poses are transformed into operation commands through a gesture understanding engine, and the interaction feedback is updated based on the operation commands. Thus, non-contact interaction can be achieved in a convenient manner, to provide convenient and practical 3D gesture interaction tools. © 2024 Copyright held by the owner/author(s).

关键词： Kalman filters

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A Green and Secure Model for Fog computing in Mobile Cloud Environments Using Containerization 3

A Green and Secure Model for Fog Computing in Mobile Cloud E...

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3rd ieee international Conference on Automation and Computation, AUTOCOM 2025

作者： Patra, Bichitrananda Seth, Kanika Mithun Kumar, S. Acharjya, Kalyan Ankayarkanni, B. Kundra, Danish Department of Computer Applications Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Internet of Things devices, and mobile computing, there is a growing demand for effective data processing where computation meets network traffic. Here, the fog computing concept has emerged to cater to this demand through a decentralized architecture that allows the processing and storing of data closer to end-users. Despite this, the dynamic and diverse fog computing ecosystem delivers security threats and resource efficiency challenges. This article introduces a solution for it, called the Green and Secure Model for Fog computing in Mobile Cloud Environments, by implementing container architecture. Containers allow applications with dependencies to be bundled together for improved portability, scalability, and resource utilization models such as GSM-FCMCE. Moreover, the model contains an intelligent security layer where encryption and data- authentication and access control paradigms-help protect volatile information from malicious attacks. In addition, the proposed model adopts green computing techniques to minimize energy utilization and increase sustainability in fog-based environments. By performing many experiments and simulations, the efficiency in terms of security achieved based on the GSM-FCMCE model works to minimize energy waste rate and increase resource utilization. It would seem quite a viable model for the cloud computing community to solve some of these fog-related issues and introduce more sustainable & secure mobile data processing. © 2025 ieee.

关键词： Authentication

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd international Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 ieee.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural network (CNN) Pest Classification

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