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International Conference on Computing and Networking technology (ICCNT)

作者： Aviral Srivastava Usha Jain Amity School of Engineering and Technology Amity University Rajasthan Jaipur India Department of Computer Science and Engineering Manipal University Jaipur Jaipur India

Increased security complexity and variety come with IoT devices. Existing security solutions generally fail to handle multiple difficulties, particularly in real-time attack detection and response. This research introduces a novel, comprehensive architecture that synergistically incorporates various cutting-edge security measures to protect IoT networks from all cyber threats. The proposed framework incorporates NodeMCU ESP8266-based real-time response, safe boot, hardware-based security modules, and zero trust architecture. It recommends incorporating blockchain for security, real-time logging and monitoring, machine learning-assisted intrusion detection, and indicators of compromise (IOCs) for incident response. For improved malware analysis and threat mitigation, integration of APIs for smooth communication with third-party services such as VirusTotal and Hybrid Analyzer is suggested. This paper also discusses data encryption, updates and patching, identity and access management, risk assessment and threat modelling, incident response and recovery, and privacy. This holistic approach to IoT security provides a robust, flexible, and scalable framework to address the changing threat landscape and growing complexity of IoT ecosystems. The proposed methodology defends IoT networks against sophisticated cyberattacks through careful analysis and empirical validation. In an increasingly interconnected world, this research could be useful for practitioners, academics, and stakeholders aiming to strengthen IoT infrastructure resilience.

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Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Prediction model using SMOTE, genetic algorithm and decision...

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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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A Comparative Analysis of Communication Efficiency: REST vs. gRPC in Microservice- Based Ecosystems

A Comparative Analysis of Communication Efficiency: REST vs....

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Ritu Shruti Arora Aanshi Bhardwaj Ashima Kukkar Sawinder Kaur Department of Computer Science & Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

This study investigates the practical aspects of evaluating the efficiency and performance of Representational State Transfer (REST) and gRPC communication protocols in microservice-based systems. Microservices revolutionize software architecture by enabling scalable applications through independently deployable services. However, effective communication between these services is crucial for maintaining system responsiveness and reliability. The paper takes a hands-on approach, drawing insights from real-world implementations and experiments. It offers a pragmatic overview of microservices architecture, highlighting the pivotal role of communication protocols in facilitating seamless interactions among distributed components. Furthermore, the study delves into the technical intricacies of REST and gRPC, exploring aspects such as message formats, serialization mechanisms, and transport protocols. Through empirical analysis, the paper evaluates the latency performance of REST and gRPC across various communication tasks under different workloads and network conditions. These insights provide practical guidance for developers and architects in selecting the most suitable communication protocol based on specific project requirements and performance constraints, ultimately enhancing microservice communication efficiency and reliability.

关键词： Transport protocols Technological innovation Protocols Ecosystems Microservice architectures computer architecture Throughput

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A Multi-model Software Defect Recommendation System

A Multi-model Software Defect Recommendation System

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vikas Suhag Sanjay Kumar Dubey Bhupendra Kumar Sharma Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India Northern India Textile Research Association Ghaziabad Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The proposed recommendation system has integrated data collection framework, which can collect repository source data and thus generate base dataset for machine learning and artificial neural network models. So proposed framework provides an end-to end solution for software defect recommendation on python projects in GitHub repository. Recommendation system is comprised of multiple components like data collection system, hybrid feature selection strategy, machine learning models and deep learning models, commit monitor. Framework has been validated on several projects from GitHub repository and outcome of recommendation is favourable for usage in real projects.

关键词： Quality assurance Data collection Software Data models Informatics Recommender systems Monitoring Software development management Python Testing

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Improving Sentiment Classification on Restaurant Reviews Using Deep Learning Models

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Procedia computer science 2024年 235卷 3246-3256页

作者： Ratna Nitin Patil Yadvendra Pratap Singh Shitalkumar Adhar Rawandale Sofia Singh Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune 411048 India Computer Science and Engineering Department Manipal University Jaipur 303007 India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Technology Pune 411044 India School of Engineering and Technology Amity University Noida 201301 India

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively.

关键词： Natural Language Processing Sentiment Analysis CNN Bi-LSTM

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Driver Drowsiness Detection using AI Techniques 9

Driver Drowsiness Detection using AI Techniques

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9th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Manchanda, Tushar Singh Singh, Gurdit Singh, Shailendra Narayan Amity University Amity School of Engineering and Technology Department of Computer Science Uttar Pradesh Noida India

ISBN: (纸本)9781665417037

The modern age technology has evolved at a high pace to make human lives at ease. The leading mode of transportation and transferring of goods from one place to another in 1900's was bicycles, then came along motorbikes which then evolved to 4-wheeled vehicles. In today's era, it's difficult to imagine a life without a vehicle. Although it has a lot of advantages, there are definitely some life-threatening cons. According to statistics, every year around 20% of the accidents are fatigue and distraction based. Driving when one is tired makes the person prone to get involved in an accident, putting theirs as well as others life in danger on the road. Hence, some moderation is required for safekeeping of people on the road. The objective of this paper is to tackle the grave issue of drowsiness and fatigue by detecting the mouth and the eye region of the driver along with building a 'Driver Drowsiness Detector' prototype. The prototype was devised in various phases and sub steps, where python language has been utilised for recognising the face and eye region in a still image along with capturing the real time footage of the person sitting in front of the camera. Yawn Detection and EAR (Eye Aspect Ratio) were integrated and implemented in the prototype for detecting fatigue;which was further merged with CNN model to improve the accuracy of the drowsiness detection model. © 2021 IEEE.

关键词： Aspect ratio

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Analysis of Influence of the Conflict on Cyber Security and it Development

SSRN

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SSRN 2024年

作者： Dua, Yash Raj Tanwar, Sarvesh Badotra, Sumit Kurkreja, Vinay Amity Institute of Information Technology Amity University Noida India School of Computer Science and Engineering Bennett University UP Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

As digital technology becomes more important in economic and social processes, cybersecurity becomes growing critical. It plays a crucial role in our digital world because cybersecurity issues can result in ethical outcome that range from economic damage to physical harm. However, while cybersecurity measures aim to prevent harm, some of these measures may also cause harm and violate upon human rights by limiting personal freedoms. Specific areas of cybersecurity also raise ethical concerns, such as the ethics of cyberwar and ethical hacking. However, implementing cybersecurity measures can sometimes conflict with other moral values. This paper examines conflicts between cybersecurity and other values, drawing on a review of literature. The paper tells that the common approach of dividing security and privacy is not sufficient for addressing the complex ethical challenges of cybersecurity. The paper aims to show that cybersecurity ethics must be examined in a broader context that considers the values at support past security and privacy. © 2024, The Authors. All rights reserved.

关键词： Cybersecurity

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A Deep Learning Based Approach To Recognize Real Time Emotions

A Deep Learning Based Approach To Recognize Real Time Emotio...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Abhimanyu Vatsa Sumita Gupta Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida

Facial expressions is the most primary way by which a human recognizes another person’s emotion. With a rapid development in the field of Artificial Intelligence and Machine learning, research have started to develop and improve a sophisticated technology by which a machine or a computer can recognize emotions and mimic a human’s thought process for the same. The proposed methodology for the same is to make use of deep learning. Deep learning is very closely related to machine learning. It allows multiple layers of neural networks to be stacked upon one another enabling our algorithm to “think” like a human. Many mathematical models are also used to provide the best results and improve on the currently known technology. This paper discusses most of the known approaches for emotion recognition and tests out geometry based using machine learning and deep learning-based implementation providing a deep insight into both methodologies

关键词： Deep learning Geometry Emotion recognition Machine learning algorithms Face recognition Neural networks Real-time systems

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Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

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Computational Intelligence 2024年第1期40卷 e12630-e12630页

作者： Xu, Xuedong Sun, Wei Vivekananda, G.N. Shankar, Achyut Department of Mechanical Engineering School of Mechanical and Electrical Engineering Changchun Institute of Technology Changchun China School of Architecture and Design Changchun Institute of Technology Changchun China Department of CSE Madanapalle Institute of Technology & Science India Department of computer science and Engineering ASET Amity University Noida India

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency. © 2020 Wiley Periodicals LLC.

关键词： Linear programming

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Quant-Meta: Quantum Key Distribution Framework for Telesurgery in Metaverse Environment

Quant-Meta: Quantum Key Distribution Framework for Telesurge...

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IEEE Globecom Workshops

作者： Pronaya Bhattacharya Vishaka K Ralegankar Rajesh Gupta Sudeep Tanwar Joel J. P. C. Rodrigues Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India COPELABS Lusófona University Lisbon Portugal

Advanced robotics, high-speed networking, and real-time haptics have opened a myriad of opportunities to extend telesurgery in Metaverse environments. The telesurgery metaverse (TMs) offers high-quality, immersive, and interactive surgical scenarios. Despite the remarkable progress, TMs suffer from operational threats against quantum-adversarial attacks, which can jeopardize the integrity, reliability, and safety of operated patients. The paper presents a novel framework Quant-Meta, that integrates quantum key distribution (QKD) in TM through the BB84 protocol. A framework is presented for TM, where the surgeon communicates via gestures that are communicated securely to the robotic arm present in a metaverse environment. A quantum channel sets up the BB84 key distribution, and the sender and receiver agree to the shared key. Once it is set up, the communication is supported by a 6G-envisioned tactile internet (TI) channel. which assures high reliability. To ensure high precision, explainable artificial intelligence (XAI) is integrated with the trained model to predict doctor gestures. The framework is analyzed in terms of BB84 protocol design, and quantum channel parameters. The result signifies a high probability of intruder detection and a 19.87 % improvement in channel transmission rate. The quantum bit error rate also likes below 4 %, with a high sifted key rate. The results indicate the potential viability of the framework in practical telesurgical deployments.

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