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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： D Baswaraj Abdur Rahman Deependra Pandey T Bhargavi Muslimbek Ismoilov Natha Deepthi Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India School of Business & Technology Emporia State University Emporia Kansas USA Department of Electronics and Communication Engineering Amity School of Engineering & Technology (ASET) Amity University Lucknow India Department of Information Technology St. Martins Engineering College Hyderabad India Department of Electrical Engineering Fergana Polytechnic Institute Fergana Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cyber threats targeting Internet-of-Things(IoT) devices have grown in frequency and severity alongside the IoT's fast uptake and usage in the past few years. Because of this, creating a model that can detect hostile attacks and lessen security risks in IoT-devices is becoming a very important subject. Botnets pose a significant risk because they disrupt the networks that Internet of Things (IoT) devices rely on to function. An method called GA-HDLAD, which stands for genetic-algorithm with hybrid deep learning(DL), was created in this study to advance security in the IoT environment by detecting botnets. Botnet detection makes use of hybrid deep learning, which combines attention concepts, feature-extraction-techniques(FETs), and recurrent-neural-networks(RNNs). The hybrid-DL approach can identify botnet attacks, which frequently use complicated patterns. In addition, the model's RNNs capture temporal dependencies, while FETs guarantee efficient feature extraction from geographical data. To choose the hyperparameters required by the HDL method, simulated annealing (SA) is employed. This paper compares the GA-HDLAD system to other detection approaches and finds that it outperforms them experimentally using a benchmark botnet dataset.

关键词： Deep learning Integrated circuits Botnet Field effect transistors Simulated annealing Feature extraction Internet of Things Security Hardware design languages Anomaly detection

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Optimizing the Capacity of Extreme Learning Machines for Biomedical Informatics Applications

Optimizing the Capacity of Extreme Learning Machines for Bio...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： J. Logeshwaran Rajat Bhardwaj Shailaja Salagrama Abhijit Das Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Information Technology University of the Cumberland’s Williamsburg Kentucky USA Department of Computer Science and Engineering ASET Amity University Bengaluru Karnataka India School of Computer Science and Engineering RV University Bengaluru India

the paper discusses a way of growing the capability of severe deep learning machines (ELM) for biomedical informatics programs. This technique involves varying the dimensions of the training set, the variety of neurons in the hidden layer, the kind of activation function used, the range of different sorts of neurons used and the regularization techniques *** that it will maximize the ability of ELM fashions for biomedical informatics applications, the authors advocate using a stepwise seek technique to determine the fine combination of parameters. This technique entails first defining a baseline or initial configuration of ELM parameters and then iteratively enhancing that configuration via deep learning from the empirical effects of each optimization step. distinctive regularization strategies are carried out at every optimization step which could involve pruning hidden neurons as a way to reduce the complexity of the model. The authors verified their technique on publicly to be had datasets consisting of the Michigan Imputation of missing Values (MIMV) dataset and on a dataset of clinical records from the branch of Anesthesiology, branch of health and Human services.

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Behavioral biometrics for adaptive authentication in digital banking - Guard against flawless privacy

Behavioral biometrics for adaptive authentication in digital...

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2021 International Conference on Innovative Practices in Technology and Management, ICIPTM 2021

作者： Sahdev, Supriya Lamba Singh, Saurabh Kaur, Navleen Siddiqui, Laraibe Amity International Business School Amity University Noida Uttar Pradesh India ASET Amity University Department of Computer Science and Engineering Noida Uttar Pradesh India

ISBN: (纸本)9780738132891

This study throws light on the usage of Behavioral Biometrics in Digital banking arena. In current scenario more of physiological biometric modalities are being used in Digital Banking in comparison to psychological/ behavioral modalities which are difficult to record or mimic making behavioral biometrics more secure against replay attacks. This study suggests the usage of mobile screen swipe and touch data for user verification. The experiments were performed using publicly available UMDAA02 mobile swipe data set. The results of the study present a fine-tuned feature set for a swipe-based authentication system for mobile devices used for Digital Banking. It has been observed that with the suggested feature set, K-NN outperforms Naïve bays and SVM algorithms with the best EER of 14%. With the kind of performance shown by K-NN this study strongly suggests, swipe-based authentication system that can be used as a secondary layer of security in the digital banking system. © 2021 IEEE.

关键词： Authentication

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Incorporating Requirement engineering into Agile Methodologies: Challenges and Proposed Solutions

Incorporating Requirement Engineering into Agile Methodologi...

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International Conference on Signal Processing and Communication (ICSC)

作者： Prateek Srivastava Nidhi Srivastava Rashi Agarwal Pawan Singh Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow Campus Lucknow India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur India Department of Computer Science and Engineering ASET Amity University Uttar Pradesh Lucknow Campus Lucknow India

Agile software development has a good track record, but only when the project size is limited. Requirement engineering (RE) is critical because “Requirements” play a critical role at each stage of the lifecycle of software. Agile supports changing customer requirements while adding value to the business, it also comes with its own set of obstacles, many of which are connected to requirement constraints. This study seeks to uncover the issues that agile project requirements engineers confront. Many research studies on requirement issues have been undertaken, all of which are compromised in some way, offer no suggestions for improving the agile development process, and fail to reveal massive agile development hurdles. This paper addresses all the issues raised above and suggests possible solutions from the standpoint of requirement engineering and results can be extremely beneficial to the software business in terms of improving development processes, as well as individuals who would like to continue working in this area.

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ETMA: Efficient Transformer Based Multilevel Attention framework for Multimodal Fake News Detection

arXiv

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arXiv 2022年

作者： Yadav, Ashima Gaba, Shivani Khan, Haneef Budhiraja, Ishan Singh, Akansha Singh, Krishan Kant School of Computer Science Engineering and Technology Bennett University Greater Noida India College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Science Engineering ASET Amity University Noida India

In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality. This paper aims to overcome these limitations by proposing an Efficient Transformer based Multilevel Attention (ETMA) framework for multimodal fake news detection, which comprises the following components: visual attention-based encoder, textual attention-based encoder, and joint attention-based learning. Each component utilizes the different forms of attention mechanism and uniquely deals with multimodal data to detect fraudulent content. The efficacy of the proposed network is validated by conducting several experiments on four real-world fake news datasets: Twitter, Jruvika Fake News Dataset, Pontes Fake News Dataset, and Risdal Fake News Dataset using multiple evaluation metrics. The results show that the proposed method outperforms the baseline methods on all four datasets. Further, the computation time of the model is also lower than the state-of-the-art methods. © 2022, CC BY.

关键词： Social networking (online)

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Predicting Gender Development Index using Machine Learning 13

Predicting Gender Development Index using Machine Learning

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13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Gupta, Kavya Rani, Ritu Sharma, Arun Bansal, Poonam Dev, Amita Gandhi, Rashmi Indira Gandhi Delhi Technical University for Women Computer Science with Artificial Intelligence Engineering New Delhi India Indira Gandhi Delhi Technical University for Women Center of Excellence Artificial Intelligence New Delhi India University for Women Indira Gandhi Delhi Technical New Delhi India Amity University Cse Aset Uttar Pradesh Noida India

ISBN: (纸本)9781665462631

The Gender Development Index (GDI) is often considered a "gender-sensitive augment to the HDI". It addresses inequalities in life expectancy, education, and income among genders. The primary objective of this paper is to predict the GDI Values of various countries by using attributes that are highly correlated with the GDI, using Machine Learning Algorithms such as Linear Regression, Ridge Regression, and K Nearest Neighbours. We have also compared the prediction accuracy of these models to determine the optimal one and we found out that Linear Regression was the most accurate model with 97.30% accuracy. We can further use this model to improvise existing policies to reduce the gender gap in our society. © 2023 IEEE.

关键词： Machine learning

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Sentiment Analysis of Ayodhya Verdict using Twitter

Sentiment Analysis of Ayodhya Verdict using Twitter

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2020 International Conference on Intelligent engineering and Management, ICIEM 2020

作者： Malhan, Yash Singal, Swati Amity University Uttar Pradesh Department of Computer Science and Engineering ASET Noida India

ISBN: (纸本)9781728140971

Sentiment Analysis is quickly emerging on top in research field of computer science. There are several forms of determining the sentiment of people's opinions, either through reviews, feedback, blogs etc. However, in order to know opinions of different people a platform is needed. Twitter is most desired one as it has user-friendly interface that is focused for engagement of audience online which include vast variety of people as students, professionals, celebrities, corporations and politicians. The proposed work aims to get tweets of various political leaders on Ayodhya Verdict using Python tools and libraries, and after fetching, analyze them as Positive, Negative or Neutral and plotting the results in graphical representation. © 2020 IEEE.

关键词： Sentiment analysis

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Performance Evaluation of PO, SO and SHO Algorithms for Epileptic Seizer Detection

Performance Evaluation of PO, SO and SHO Algorithms for Epil...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Mohapatra, Sumant Kumar Mallik, Puspanjali Swain, K.P. Bhardwaj, Rajat Mehta, Gaurav Verma, Rajit Trident Academy of Technology Department of Etc Bhubaneswar India Siksha 'O' Anusandhan University Department of Cse Bhubaneswar India Amity University Aset Department of Computer Science and Engineering Bengaluru India Chitkara University School of Engineering and Technology India Mm Institute of Management Mullana Haryana Ambala India

ISBN: (纸本)9798331530259

Diseases originated due to the disorder in the neurons present in the brain part of the body is called epilepsy and the symptom observed in other parts of the body is called a seizure. Not only it is a typical disease in human beings but animals are also suffered from this disease. To live an epilepsy-free life, neurologists and researchers contribute their best effort to devise new techniques and presently metaheuristic optimization algorithms and machine learning algorithms are integrated into this field to design powerful EEG devices for accurately detecting the presence of EEG signals. This research paper presents the comparison of three metaheuristic optimization algorithms used in designing EEG detection. In addition, these three algorithms are integrated with Extreme Learning Adoptive Neuro-Fuzzy-Inference System (ELANFIS) as the classifier and Wavelet Packet Transform as the feature extraction technique. For continuing research work, this experiment uses the Bonn University EEG dataset. After a thorough analysis, it is observed that WPT-PO-ELANFIS is the best-performing technique among these three with average accuracy, sensitivity, specificity, and area under curve values as 98.42, 96.75, 97.97.97.65, and 0.989. This research work also can be tested with other popularly available datasets and integrated with real-time applications. © 2025 IEEE.

关键词： Fuzzy neural networks

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Performance Evaluation of PO, SO and SHO Algorithms for Epileptic Seizer Detection

Performance Evaluation of PO, SO and SHO Algorithms for Epil...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sumant Kumar Mohapatra Puspanjali Mallik K.P. Swain Rajat Bhardwaj Gaurav Mehta Rajit Verma Department of ETC Trident Academy of Technology Bhubaneswar India Department of CSE Siksha ‘O' Anusandhan University Bhubaneswar India Department of Computer Science and Engineering ASET Amity University Bengaluru School of Engineering and Technology Chitkara University India MM Institute of Management Maharishi Marksandeshwar Deemed to be University Haryana India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

关键词： Performance evaluation Machine learning algorithms Accuracy Sensitivity Metaheuristics Neurons Electroencephalography Wavelet packets Real-time systems Classification algorithms

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A Review on Multi-Focus Image Fusion Techniques in Surveillance Applications for Image Quality Enhancement 5

A Review on Multi-Focus Image Fusion Techniques in Surveilla...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kaur, Paramjeet Panwar, Garima Uppal, Nilesh Singh, Prabhishek Shivahare, Basu Dev Diwakar, Manoj Amity University Uttar Pradesh Greater Noida India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Amity University ASET Department of CSE Uttar Pradesh Greater Noida India Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Image fusion is process of aggregating two or more than two degraded images into single image whose overall image quality is better than the previous input images. Multi-focus image fusion (MFIF) is one of the kinds of image fusion. MFIF is process of aggregating two or more than two multi-focused images (left/right/top/bottom focused) into single fused image whose overall image quality is better than input images. Out of various applications, surveillance is the major application of MFIF. Weak images may be enhanced by image fusion technique. This paper discusses various multi-focus image fusion techniques. The main discussed techniques are based SWT, DWT, PCA, CNN, Method noise etc. These techniques work under the environment of visual sensor network (VSN), where all the surveillance cameras are attached to single central computer system. This system performs the operation of MFIF. This paper also comparatively discusses the techniques based on various parameters like objective, methodology, merits, and demerits. © 2022 IEEE.

关键词： Image fusion

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