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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Misha, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Amity University Uttar Pradesh Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Pranveer Sing Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350335095

Internet of Things (IoT) applications, such as e-healthcare departments have grown tremendously where devices gather patient data and instantly transmit it over a distance to servers. Despite its huge advantages, IoT in the healthcare sector has acquired little consideration, largely because of the threats of unauthorized access to private health data made possible by the weak wireless communication channel. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the Internet of medical things (IoMT) that is based on zero-knowledge proofs (ZKP). The research will include the comparison between computational delays in traditional protocols and ZKP protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP. © 2023 IEEE.

关键词： Authentication

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Variable Neighborhood Search Based Human Learning Optimization Algorithm for Secure Data Analysis and Computing

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SN computer science 2024年第5期5卷 557页

作者： Ahmed, Feroz Kumar, Sumit Singh, Pradeep Kumar Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Central University of Jammu J&K Samba India

The data analysis in medical field is a very crucial task in order to gain insights from a large collection of data. The analysis of data comprises of several well-defined processes like data collection, data preprocessing and exploratory data analysis techniques to compute undiscovered patterns and trends. Several statistical and machine learning algorithms are utilized to discover meaningful patterns and information. Clustering is an unsupervised machine learning technique that aims at discovering patterns in data to form clusters of similar data objects. In this work, an effective diagnostic model is being proposed based upon human learning optimization (HLO) algorithm. Since the HLO algorithm have shortcomings like trade-off issues and local optima, an improved search mechanism and variable neighborhood strategy is proposed in HLO to deal with them. The proposed technique is evaluated using real-world benchmark clustering datasets. The results are assessed using well-known performance parameters like accuracy, detection rate and f-measure and compared with several existing algorithms while detection rate is improved by 4%. The outcomes obtained reflects the supremacy of the proposed technique in effective cluster analysis. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024..

关键词： Clustering Data analysis Human learning optimization Meta-heuristic algorithm

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Enhanced ML Based Content-Based Image Retrieval for Mobile Devices

Enhanced ML Based Content-Based Image Retrieval for Mobile D...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Meenakshi, K.M. Singh, Kirti Dilip Batta, Priya Chauhan, Rakhi Bhargava, Deepshikha Lohani, Bhanu Prakash Amity School of Engineering & Technology Amity University Greater Noida India Chandigarh University Department of Computer Science & Engineering Mohali India Geeta University Department of Computer Science & Engineering Haryana Panipat India

ISBN: (纸本)9798350379716

Image retrieval systems based on content can be developed for mobile applications that provide users with a seamless and efficient way to search for images based on their content. The development of CBIR systems for mobile applications presents unique challenges and opportunities. Mobile devices typically have limited computational resources, storage capacity, and network bandwidth compared to traditional computing platforms. Therefore, CBIR systems designed for mobile applications must be lightweight, efficient, and optimized to run smoothly on these devices. The Adaboost algorithm progressively learns weak classifiers and aggregates them to construct a robust final classifier. Subsequently, a weak learner is introduced, and the data undergoes reweighting. Misclassified test images are assigned greater importance while correctly classified ones are downweighted. Consequently, subsequent weak learners prioritize the query images previously misclassified by their predecessors. The average precision is increased in further iterations to 95.6% from the initial iteration of 94.9%. Average recall shows a little more improvement from the previous set by increasing from round 1 with 94.1%, and increasing to 95.9% towards the last round of iteration. The F-Measure keeps fairly stabilized at 95.1478 after the first round. The time taken by every iteration reduces slightly from 3. © 2024 IEEE.

关键词： Content based retrieval

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Heart Disease Analysis and Prediction Using EDA and ML Classifiers 5

Heart Disease Analysis and Prediction Using EDA and ML Class...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Khan, Humra Singh, Pawan Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

Heart is one of the most important organs as blood is pumped throughout the body by the heart. Heart disease is widely regarded as the illness that kills individuals most quickly. To prevent catastrophic risks and diminish heart-related problems, prompt identification of heart disease is essential. The disease prediction system helps clinicians identify serious illnesses as early as possible. This research paper aims to provide a complete examination of the features contributing to heart disease with respect to by means of exploratory data analysis and predict the possibility of heart disease with the use of machine learning algorithms. Through EDA, it was discovered that, of all these features, the number of major vessels (0-3) stained by fluoroscopy, different types of chest pain, greatest heart rate reached, exercise-induced angina, exercise-induced ST depression compared to rest, and inclination of the peak exercise ST segment are the ones that have the greatest impact on the diagnosis of heart disease. Utilising the UCI heart disease dataset containing the features, three machine learning classifiers are implemented: decision tree (DT), random forest (RF), and K-nearest neighbour (KNN). When compared to other heart disease prediction models, Random Forest has the best rate of accuracy (85.25%). © 2023 ACM.

关键词： Forecasting

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Transfer Learning Induced Communication Enhancement for People with varied Disabilities 5

Transfer Learning Induced Communication Enhancement for Peop...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Singh, Anupreet Arora, Deepak Department of Computer Science & Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

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Network Intrusion Detection for IoT Devices Using Deep Learning 10

Network Intrusion Detection for IoT Devices Using Deep Learn...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, Mohit Dubey, Sanjay Kumar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350382471

Cybersecurity has seen widespread adoption across various domains, encompassing critical business infrastructure, residential settings, personal devices, and machinery. This has led to the development of innovative capabilities to effectively address security challenges within the realm of Internet of Things (IoT) devices. Notably, new security measures, including the application of deep learning for intrusion detection, have been introduced. Recent research primarily focuses on enhancing algorithms for a deeper understanding of IoT security. This research explores intrusion detection techniques employing deep knowledge mining. It involves a comprehensive analysis of various deep knowledge mining methods, comparing their overall performance. The objective is to identify a precise approach for implementing intrusion detection in the IoT context. The research leverages deep learning methodologies, specifically convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Furthermore, it incorporates an extensive dataset tailored for IoT intrusion detection. The empirical results of this study are thoroughly examined and compared against existing IoT intrusion detection techniques. Notably, the proposed approach demonstrates superior accuracy when compared to other methods. © 2023 IEEE.

关键词： accuracy convolutional neural network deep knowledge acquisition gated recurrent unit intrusion detection IoT long short-term memory

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Performance Evaluation of HSRP and GLBP Over OSPF and RIP Routing Protocols 9th

Performance Evaluation of HSRP and GLBP Over OSPF and RIP Ro...

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9th International Conference on Signal Processing and Integrated Networks, SPIN 2022

作者： Agarwal, Aayush Sharma, Shilpi Xavier, Evin Tom Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida UP Noida India

ISBN: (纸本)9789819913114

After the rise of Covid-19 in the whole world, most of the organizations were forced to shift their business and services online for the users. Thereby, for the businesses and services to run smoothly, full-time availability of the network is a must. But there may arise situations in which availability might not be present. This problem is solved by using redundancy network protocol in network infrastructure. Among the routing and redundancy protocols used, it is prudent to evaluate which routing protocol gives best performance with which redundancy protocol. This paper aims to evaluate the performance of the most widely used routing protocols OSRP and Rip with Cisco Proprietary redundancy protocols HSRP and GLBP. The result of this study indicates that OSPF and RIP work best with HSRP and GLBP, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Redundancy

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Optimizing Integrated Features in Mobile Devices using Kotlin Framework 2

Optimizing Integrated Features in Mobile Devices using Kotli...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Tiwari, Vaibhav Arora, Deepak Amity School of Engineering and Technology Lucknow Amity University Department of Computer Science &engineering Uttar Pradesh India

ISBN: (纸本)9798350347579

Every person's goal is to be both physically and psychologically healthy. Existing applications offer personalized exercise and workouts to meet the varied user requirements. People who lack the time to visit fitness centers have found the apps to be a great relief. Also, it offers totally free plans and workout recommendations. Many people have begun using these applications after realizing how important they are to their everyday lives. This study tries to comprehend how users feel about fitness programs that are available for mobile devices. This study mainly focuses on the efficiency of fitness applications in terms of many aspects including time, cost, and accessibility. It also explores the benefits of mobile applications over workout facilities. The study also makes suggestions for ways to boost and enhance user engagement with app usage. © 2023 IEEE.

关键词： Cost effectiveness

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Lucknow Campus Amity School of Engineering and Technology Department of Computer Science and Engineering India Indian Institute of Information Technology Allahabad Department of Information Technology India

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition Variational Autoencoder

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Deep Learning Model for Identification and Classification of Web based Toxic Comments

Deep Learning Model for Identification and Classification of...

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2023 International Conference in Advances in Power, Signal, and Information technology, APSIT 2023

作者： Shukla, Anubhav Arora, Deepak Amity School of Engineering and Technology Lucknow Amity University Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350339369

Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste are made by anonymous people over the internet, and it is quite a task to keep these comments under control. So, the objective was to develop a Machine Learning Model to help identify these comments. A Deep Learning Model (a sequential model) was made and it was trained to identify and classify a comment based on whether it is an apt comment or not. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is particularly well-suited for modeling sequential data, such as text. LSTMs are capable of modeling long-term dependencies in sequential data. In the case of text classification, this means that LSTMs can take into account the context of a word or phrase within a sentence, paragraph, or even an entire document. LSTMs can learn to selectively forget or remember information from the past, which is useful for filtering out noise or irrelevant information in text. LSTMs are well-established in the field of natural language processing (NLP) and have been shown to be effective for various NLP tasks, including sentiment analysis and text classification. Binary cross-entropy is a commonly used loss function in deep learning models for binary classification problems, such as predicting whether a comment is toxic or not. Binary cross-entropy is designed to optimize the model's predictions based on the binary nature of the classification task. It penalizes the model for assigning a low probability to the correct class and rewards it for assigning a high probability to the correct class. The loss function is differentiable, which allows gradient-based optimization methods to be used during training to minimize the loss and improve the model's performance. Binary cross-entropy is a well-established loss function that has been extensively used

关键词： Sentiment analysis

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