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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

computer Systems science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

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computer Systems science & engineering 2023年第3期44卷 2147-2163页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

Android devices are popularly available in the commercial market at different price levels for various levels of *** Android stack is more vulnerable compared to other platforms because of its open-source *** are many android malware detection techniques available to exploit the source code andfind associated components during execution *** obtain a better result we create a hybrid technique merging static and dynamic *** this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing *** the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given *** Android Sensitive Permission is one major key point to be considered while detecting *** select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or *** goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

关键词： Zero-day exploit hybrid analysis principal component analysis supervised learning smart cities

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Assessing Degradation Levels of Palm Leaf Manuscripts with Random Forest Using Gabor Features 8th

Assessing Degradation Levels of Palm Leaf Manuscripts with R...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Kowshik, Achyuta Siva Sai Sindhur, Ambati Sai Reddy, Annem Gnaneswara Ganesh, Manasa Sivan, Remya Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

This experiment focuses on employing machine learning techniques to create an automated system for classifying palm leaf manuscripts into three distinct categories based on their degradation levels: good, bad, and medium. The study collects a wide range of palm leaf samples at different degradation levels. Using advanced machine learning algorithms and an efficient feature extraction method, a model is created that accurately classifies palm leaves by level of degradation. The research incorporates Gabor feature extraction and statistical classifiers to train the extracted features. The classifiers used are k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), XG Boost (XGB), and Ada Boost (AB). After employing SMOTE, normalization, and feature selection, RF obtained the highest mean accuracy of 87.25% and a standard deviation of 0.08 in comparison with the other classifiers. © IFIP International Federation for Information Processing 2025.

关键词： Random forests

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Enhancing Healthcare AI Sensitivity through Affective computing: Analyzing Human Emotions 1

Enhancing Healthcare AI Sensitivity through Affective Comput...

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

作者： Singh, Rahul Kumar, Avadhesh Sagar, Shraddha Sharma, Vishnu Galgotias University School of Computing Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350356816

Affective computing (AC), also known as emotion AI, is a concept that applies modern technology to help systems to recognize and process human emotions and their feelings. Several uses of affective computing have been demonstrated, including marketing, healthcare, driving assistance, and education. Linear Support vector machines (SVM), k-nearest neighbor (k-NN), Hidden Markov models (HMMs), Gaussian mixture models (GMM), discriminate classifiers (LDC), decision tree algorithms and artificial neural networks (ANN), are the classifiers that are most frequently used in research on identifying and categorizing human emotions. Selecting the right classifier can significantly increase the system's overall efficiency, as numerous studies have conclusively demonstrated. Through use of cameras, microphones, skin sensors, or other devices, affective computing gathers signals from human users, gathering data on their facial expressions, voice tones, gestures, and other characteristics that can hint at their emotional states. The system deduces the user's emotional state from those data points. This study emphasizes on the fundamentals of computing emotions. © 2024 IEEE.

关键词： Support vector machines

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RIDynaQ: A DynaQ Based System for Reading Impairment Detection 8th

RIDynaQ: A DynaQ Based System for Reading Impairment Detecti...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Surisetty, Hima Varshini Gottimukkala, Sarayu Varma Amudha, J. Department of Computer Science and Engineering Amrita School of Computing Amrita Nagar Choodasandra Karnataka Bengaluru India

ISBN: (纸本)9789819721467

Dyslexia or reading impairment is a reading disorder that can have an effect on both children and adults. Though reading impairment affects the reading abilities of an individual it does not have an effect on the intelligence quotient of the individual. It has been proven that eye-gaze behavior can be used as a good indicator of the reading abilities of a person. Therefore, using eye-gaze behavior and the eye-gaze patterns as a determinant of a good reader and a poor reader can help with the identification of reading impairment. An early detection of reading impairment can help the person in getting the required help as soon as possible. In this work, the detection of reading impairment has been modeled as a reinforcement learning problem using a model-based learning approach. The proposed model is named as RIDynaQ which stands for Reading Impairment detection using DynaQ wherein the eye-gaze behavior of both dyslexic and non-dyslexic children was considered and the optimal gaze targets and the optimal policy for reading for both good and poor readers were obtained. Also, a comparison was drawn amidst the pre-existing model-free approach and model-based approach. The main difference between the existing Q learning based approach and RIDynaQ is the use of simulated experiences and developing a model of the environment which does not exist in Q Learning based approach but is used in the RIDynaQ model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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IoT Device for Food Storage 8

IoT Device for Food Storage

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Mandal, Lopa Vardhan, Guttapalem Reddy Vishnu Bharani, G.S. Ahmed, Jawad Kavin, K. Reddy, Nandi Sasidhar Alliance School of Advanced Computing Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520762

The integration of IoT technology provides realtime, data-driven monitoring that reduces waste and maintains the quality of the food, changing the way food is preserved and managed. This research focuses on the development and implementation of an Arduino-based IoT device for monitoring freshness, temperature, and humidity in storage food environments. Equipped with an MQ-4 methane sensor and DHT11 sensor, the system detects spoilage indicators and environmental conditions and opens an exhaust fan if the moisture has been in excess. The BLYNK IoT app provides instant information to the user about storage conditions, thus ensuring efficient and proactive food management. This product finds answers to some of the main food-related storage challenges by offering scalable and effective solutions for enhanced freshness and reduced spoilage. © 2025 IEEE.

关键词： Spoilage

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Deepfake Image Detection Using Light-Weight Attention Integrated MobileNetV3 Model 2nd

Deepfake Image Detection Using Light-Weight Attention Integr...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Harshitha, Talluri Simhadri, Tanya Jaswanthi, Thadakuluru Rayvanth, N. Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031845420

With sudden proliferation in evolving Deepfake technology to manipulate media content, identity theft etc., several modern deep learning based solutions are being continuously developed for Deepfake detection, distinguishing the manipulated media. Therefore, the work presented in this paper proposes a novel and real-time MobileNetV3 based Deepfake detection model. With the fine-tuning of MobileNetV3 using additional fully connected layers and self-attention mechanism placed optimally, the proposed model proved effective in concentrating on crucial image regions over relatively lesser important features. The integration of self-attention layers had a direct impact on the model’s capacity to identify the artifacts manipulated during the generation of Deepfakes, thereby, alleviating the detection efficiency. The work also implemented and trained five widely used and recent convolutional models including XceptionNet, InceptionV3, EfficientNetB0, MobileNetV3, and InceptionResNet for best baseline model selection and comparisons against the proposed approach. The proposed model outperformed all other state-of-art models by a margin of 3% with an F-score = 98.46%. The proposed model also has minimalistic memory and time requirements supporting the real-time applicability of the model even on mobile devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Image Captioning with Multiple Perspectives—A Visual Context-Based Approach 5th

Image Captioning with Multiple Perspectives—A Visual Contex...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Ashwin, G. Chaitanya, V. Kishan Rohith Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

This study tackles the difficult issues of image captioning while negotiating the complexity of visual data processing. The complexity of visual data and the associated processing requirements make image captioning a daunting task. This research describes an effective method for image captioning that makes advantage of attentional mechanisms. Several models, including Transformer, VGG16, VGG19, Inception, RNN decoders, and Bahdanau’s attention mechanism, are contrasted and analyzed to demonstrate the benefits of integrating attention processes. By allowing the model to focus on the appropriate visual component, attention improves the accuracy of the generated captions. Transformer models, in particular, outperform other methods by capturing complicated relationships and delivering accurate output, and they have the highest BLEU Score of 70. The findings emphasize the significance of attention mechanisms and the relevance of selecting a suitable model architecture to maximize picture captioning performance. The variety of potential applications emphasizes the benefits and potential impact of image captioning in a broad range of scenarios. Several use cases are found beneficial, including supporting the visually impaired, improving product descriptions in e-commerce, assisting medical diagnosis, improving image search ability, and allowing effective communication and comprehension of visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Predictive Maintenance for Hard Disk Drives Using Random Forest 8th

Predictive Maintenance for Hard Disk Drives Using Random For...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Baniya, Achal Vinod, Abhay Chinta, Hitesh Surya Manathanathu Pushakara, Bhavith Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

As digital data continues to grow exponentially, the reliability of Hard Disk Drives (HDDs) becomes a critical concern for ensuring data integrity and preventing unexpected system downtimes. The research dataset contains historical SMART attribute data collected from HDDs, encompassing parameters such as spin-up time, temperature reallocated sector count and other relevant metrics. The study explored usage of various exploratory data analysis (EDA) & machine learning (ML) techniques for forecasting HDD failures. EDA techniques include Synthetic Minority Over-sampling Technique (SMOTE) algorithm for balancing class representation in training set and Principal Component Analysis (PCA) for dimensionality reduction. Diverse machine learning techniques, such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Adaptive Boosting (AdaBoost), XGBoost, Gradient Boosting (GB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), are employed to predict the hard drive failure. Local Interpretable Model-agnostic Explanations (LIME) is employed to identify which features have the greatest influence on a specific prediction, providing insights into the factors driving the model’s decisions. Random Forest exhibited superior performance of accuracy 99.9 and F1 score 0.98 compared to other models. © IFIP International Federation for Information Processing 2025.

关键词： Dimensionality reduction

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A Novel Attention-Based Method for Integrating Facial and Speech Features in Emotion Recognition 2

A Novel Attention-Based Method for Integrating Facial and Sp...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350388916

Our research introduces a novel approach for recognizing multiple emotions by leveraging perspective-based methods. This technique integrates facial features and speech characteristics obtained from different encoders to pinpoint the most significant attributes. The distinct characteristics of speech and facial expressions, along with effective size management, prove to be highly beneficial. By concentrating on specific components, the system enhances overall accuracy. Images of facial features are captured from both low and high angles to ensure a comprehensive representation. These techniques are merged through a fusion mesh, producing several feature vectors that are subsequently utilized in the classification stage for sensitivity analysis. When tested with both data sets, this algorithm surpassed existing models, achieving a weighted accuracy (WA) of 90.3%. © 2024 IEEE.

关键词： Attention-Based Method Emotion Classification Facial and Speech Features Fusion Network Multimodal Emotion Recognition Weighted Accuracy

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