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A differential approach and deep neural network based data privacy-preserving model in cloud environment

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4659-4674页

作者： Gupta, Rishabh Gupta, Ishu Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Applications National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

Data outsourcing has become indispensable to allow information sharing among multiple parties. The users do not fully trust the cloud platform since it is operated by a third party. Preserving privacy while sharing the data among different parties is a challenging task;therefore, users apply the differential privacy mechanism to protect their data. However, such protection mechanisms suffer from the problem of degradation of learning results. In this paper, the authors address the degradation of the learning results due to noise injection into user’s data through ϵ-differential privacy. A differential approach and deep neural network based data privacy-preserving model is proposed, which injects noise at an appropriate position by exploiting the properties of the Laplace transform to maintain the accuracy level. The experiments are conducted over Steel Plates Fault, Spambase, Banknote Authentication, and Monk Problem datasets for deep neural network classifier to evaluate the model’s efficiency in accuracy, precision, recall, and F1-score terms. The achieved results show that the proposed model ensures high accuracy, precision, recall, and F1-score up to 99.75%, 99.72%, 99.72%, and 99.72%, and improvement up to 19.34%, 30.67%, 29.39%, and 32.11%, respectively, as compared to existing approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

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Two-Stage Model for Copy-Move Forgery Detection 1st

Two-Stage Model for Copy-Move Forgery Detection

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Kumari, Ritesh Garg, Hitendra Chawla, Sunil Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9789811984921

Digital images can be tempered or modified very easily with many tools available on the internet. Nowadays some software not only enhance and retouch but also copy-move objects of the image in such a way that is hard to differentiate. Image forgery detection deals with the recognition of tempering like splicing, copy-move, and other modifications done on the image. One of the most common ways of passive forgery detection is the block-based copy-move technique. In this paper, forgery detection is achieved in two parts. In the first part, Copy-Move forgery is classified by support vector machine (SVM) and features are extracted by local binary pattern (LBP) and discrete cosine transform (DCT). However, the region of forgery is calculated in the second part with the help of the Gray-level Co-occurrence Matrix (GLCM) through overlapping blocks. The whole experiment and evaluations are performed on the CoMoFoD image data set. The performance is measured in terms of accuracy and sensitivity. The promising result indicates the effectiveness of the model with enhanced accuracy of 97.82%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Digital Evidence Management: Modern Techniques in Digital Forensics 3

Digital Evidence Management: Modern Techniques in Digital Fo...

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

作者： Venkataramanan, N. Jayalakshmi, V. Anitha, S. Ganesan, R. Jenifer, V. Drbccc Hindu College Department of Computer Applications Pattabiram Chennai600072 India Department of Computer Science and Applications Ramapuram600089 India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai600119 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Department of Computer Science and Applications Ramapuram Campus 600089 India

ISBN: (纸本)9798331509675

This research study reviews the significance of digital forensic analysis in modern criminal investigations, highlighting the way it complements conventional methods of inquiry and the difficulties it encounters in the information era. To solve crimes effectively, investigators must combine digital knowledge with traditional forensic techniques. This will enable them to use evidence from many different types of digital and physical sources. The specific intricacies of digital crime scenes need specialized methods for the proper storage and analysis of digital evidence to identify criminal intent and patterns of conduct. To propose Modern Techniques for Digital Evidence Management [MT-DEM] based Digital Forensics Service. Multidisciplinary teams and cutting-edge technology are becoming necessities in digital forensic investigation as the field adjusts to new threats like data encryption and massive amounts of data. The admissibility of evidence in the legal world depends on digital forensic procedures being transparent and adhering to set criteria. Digital forensic analysis has become an indispensable tool for contemporary investigators with the rise of digital evidence. © 2025 IEEE.

关键词： Forensic science

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Deep Neural Networks for Comprehensive Multimodal Emotion Recognition

Deep Neural Networks for Comprehensive Multimodal Emotion Re...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Tiwari, Ashutosh Kumar, Satyam Mehrotra, Tushar Singh, Rajneesh Kumar Sharda University School of Engineering and Technology Department of Computer Science and Engineering India Sharda University School of Engineering and Technology Department of Computer Science & Applications India

ISBN: (纸本)9798350323887

Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It's crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below. © 2023 IEEE.

关键词： Emotion Recognition

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Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Raj, Vinay Suresh, Satharla Phridviraj, M.S.B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which is handed over to the third parties. To ensure the security of the data and secure private communication between the services, hybrid cryptographic algorithms play a predominant role by combining the best security features of symmetric and asymmetric encryption techniques. In this work, two hybrid cryptographic algorithms are proposed namely 2-tier and 3-tier models and the performance of the two algorithms are analyzed in terms of encryption and decryption time along with the file size. The obtained results are also compared with an existing hybrid cryptographic model and it is observed from the results that the proposed hybrid model outperforms the existing model on both the 2-tier and 3-tier models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Artificial Intelligence for Cancer Diagnosis and Prognosis: Current Status and Future Directions 14

Artificial Intelligence for Cancer Diagnosis and Prognosis: ...

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

作者： Negi, Anushree Gupta, Preeti Pradhan, Rahul Dit University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798350335095

The medical field and public health care has achieved radical progress in the last age, the original reasons behind abundant conditions were disclosed, novel symptomatic forms were devised, and new cures were developed. The second leading cause of death is cancer. worldwide afflicting approximately six out of every ten people. Variants of the lungs and colon of all cancers are those that prevalent and most fatal. However, detecting the disease in its infancy increases the odds of survival dramatically. Over the last three decades, the number of cancer victims and the scope of cancer survivability research has increased. This chapter provides a review of all types of cancer prediction with interventional and observational cancer survivorship research efforts. The treatment of cancer type is increasingly required in early cancer research. The advancement of healthcare research has been boosted by artificial intelligence. Researchers have developed program to help cancer identification and prognosis due to the availability of open-source healthcare statistics. In these conditions, DL and ML models give a dependable, quick, and effective method for dealing with such difficult diseases. Cancer categorization is a contentious issue in medicine since it allows for precise and effective diagnosis and promotes a favorable outcome in medical therapies. © 2023 IEEE.

关键词： Diseases

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Gastrointestinal Lung Cancer Disease Classification via Hybrid Deep Architecture Trained with Improved Pattern Features 5

Gastrointestinal Lung Cancer Disease Classification via Hybr...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Pradeep Kumar Rout, Subhashree Padhy, Neelamadhab Mohanty, Jnyana Ranjan Beura, Mamata Du School of Computer Engineering Odisha Bhubaneswar India Giet University Department of Computer Science and Engineering Odisha Gunupur India Du School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

In the initial stage of lung cancer, GI tract is not frequently the position of metastases. It may be challenging to distinguish pulmonary metastases from the gastrointestinal (GI) tract and primary lung cancer. The following stages are followed to classify the gastrointestinal lung cancer disease: Image acquisition, preprocessing, feature extraction, and classification. The process of collecting images is called image acquisition. As per the work, the benchmark dataset is under consideration. Gaussian filtering is used to preprocess the input image for further processing. Subsequently, the features that are retrieved in the Feature Extraction step include Shape feature, Multi texton features, Improved median Binary Pattern, and Local Gradient Increasing Pattern (LGIP). A hybrid model trained with the extracted features will be used for classification, which integrates Squeezenet and LSTM models. The results of the proposed work is compared over the conventional models in terms of different performance measures. © 2025 IEEE.

关键词： Lung cancer

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Hyperparameter Optimization of Software Quality Model Using Improvised Genetic Algorithm and EEM 1

Hyperparameter Optimization of Software Quality Model Using ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rakhi Sharma, Vishnu Kaushik, Neeraj Malhotra, Ritu Rauthan, Hemlata Delhi Institute of Higher Education Department of Computer Application Greater Noida India ITS Engineering College Department Of Computer Science and Engineering Greater Noida India Galgotias College of Engineering and Tehnology Department of Computer Applications Greater Noida India GNIOT Institute of Professional Studies Department of Computer Applications Greater Noida India Delhi Institute of Higher Education Department of Computer Applications Greater Noida India

ISBN: (纸本)9798350356816

Software quality analysis has become one of the topmost concerns in the modern world and requires the massive attention of developers and testers during software development. Software quality analysis is a multidimensional notion and one attribute may not describe the software product's overall quality. Software developer's objective is to build a software product that attains the highest quality which depends on multifarious attributes namely functional suitability, maintainability, etc. However, analysis of software product quality is challenging in both the development and maintenance phases. This research is aimed at hyperparameter optimization of the software quality model using an improvised genetic algorithm and an enhanced ensemble model (EEM) approach. In this research, there has been shown the optimal possibility of software quality model hyperparameter optimization using the modified architecture of genetic algorithm along with EEM. To validate the performance of this suggested approach, PROMISE Repository datasets have been utilized. As a result, this proposed approach achieves all performance metrics very optimal. The evaluated values of accuracy, precision, recall, and F1- score are 99.45%, 98.83%, 97.28%, and 99.37%, respectively. In the future, this approach may be integrated with modified architectures of software quality models for automated software product quality analysis. © 2024 IEEE.

关键词： Software quality

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