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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Deva Kirubai, J. Christina Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Cybercrime is a major concern in today's cyber world, and immediate action must be taken to prevent it. Traditional cybersecurity systems are vulnerable to acclimatization issues, a high percentage of false positive alerts, and so forth. To address these problems, the study proposes a cutting-edge technique that blends CNNs with transmission characteristics for anti-cybercrime policy. The CN-based system is designed to retrieve network traffic data in real-time, with the reliability of identified hostile activity proportional to the minimizing of false alarms. The system includes components such as data collection and preprocessing, CNN architecture design, training, real-time analysis, and adaptation to emerging threats. Superior performance characteristics can be seen in comparison to existing systems, including 0.90-0.92 greater accuracy, 0.89-0.94 precision, 0.97-0.98 recall, and 0.98-0.93 F1-score with a ROC AUC score of 0.97. Furthermore, the proposed system has faster detection speeds (1200 packets per second) and lower false positive rates for various cyber threat types such as malware (0.03), phishing assaults (0.05), and DDoS attacks (0.02). Thus, the proposed system represents a big step forward in the evolution of cyber security, adopting more active threat identification and mitigation, thereby strengthening existing network security and exposing it to fewer threats. © 2024 IEEE.

关键词： Phishing

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A deep learning-based approach for the identification of selected species of genus Euphorbia L.

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International Journal of Information Technology (Singapore) 2023年第5期15卷 2435-2444页

作者： Gautam, Neelam Sidhu, M.C. Kumar, Anuj Department of Botany Panjab University Chandigarh 160014 India Department of Computer Science and Applications Panjab University Chandigarh 160014 India

The classification of Euphorbia species is a challenging task due to their diverse growth forms and morphological features. In this paper, we have used the transfer learning approach to classify 10 species of Euphorbia by finetuning the five state-of-the-art architectures, namely DenseNet121, InceptionV3, MobileNetV2, ResNet50V2 and Xception. We have used self-collected dataset of Euphorbia plant species by taking the images in their natural habitats. Among all five models used, Xception has achieved maximum accuracy of 93%, followed by DenseNet121 (87%), ResNet50V2 (81%), MobileNetV2 (79%), and InceptionV3 (77%) on the held-out test set. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Accuracy Automation Convolutional neural network Medicinal Plants Transfer learning

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Analysis and Interpretation of Adolescent Multi Relationship and Privacy during COVID-19 Pandemic 6

Analysis and Interpretation of Adolescent Multi Relationship...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Mukherjee, Saurabh Rahman, Khandakar Faridar Shukla, Urvashi Prakash Gupta, Sandhya Sharma, Karuna Jangid, Divya Paharia, Nitin Banasthali Vidyapith Department of Computer Science Rajasthan India Banasthali Vidyapith Department of Psychology Rajasthan India Kaligna University Department of Computer Science Raipur India Prestige Institute of Management and Research Department of Computer Applications Gwalior India

ISBN: (纸本)9798350346961

Closures of schools in the advent of COVID-19 all around the world have affected nearly about 87% of students in different aspects of life. The importance of school life and its impact on the social and psychological well-being of an adolescent has left a deep and evident impression. As per the Indian population statistics, we embrace the largest adolescent population in the world. The COVID-19 pandemic had a significant impact on the lives of millions around the world. The authors through an exhaustive survey specially designed to relate the present mental state and well-being have analyzed the critical phase. 315 participants that consisted of both males and females take up the survey. 'What we analyzed is the fact regarding the association between Stress anxiety and Depression among the Adolescent in Covid - 19 situation.'. © 2023 IEEE.

关键词： COVID-19

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

作者： Anand Kumar, M. Purohit, Kamlesh Chandra Singh, Anuj Department of Computer Applications Graphic Era Deemed to be University Dehradun India Department of Computer Science Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9789811984921

The concept of bringing fundamental theories and analytical procedures to medicine and biology is known as biomedical engineering. Bio-artificial intelligence medical engineering is one of the newest and fastest-growing branches of research. From the adoption of medical devices to diagnostic expert systems, it can be profitable in the field of healthcare. These gadgets and expert systems generate data that are multidimensional and erratic. The use of deep learning (DL) algorithms in those devices will be useful for signal analysis and disease detection. Multiple levels of neural networks are used in DL, which are a subset of machine learning. It can learn features on its own. Strokes are one of the deadly diseases worldwide, impacting a huge percentage of the population. Stroke-related diseases require immediate medical attention, which requires emergency treatments. If it was treated in the initial stages, then brain injury and other consequences can be avoided. The main objective of this work was to evaluate different machine learning algorithms to find a suitable approach for stroke-related diseases prediction applications. This research work evaluates different machine learning algorithms, namely logistic regression, random forest, support vector machine (SVM), KNN, and the decision tree for predicting and identifying stroke and its related diseases. Finally, deep belief networks are applied for the symptom dataset to get results that are more accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Exploring and Evaluating Energy-Efficient Clustering Schemes in AODV: An Implementation and Comparative Study 12th

Exploring and Evaluating Energy-Efficient Clustering Schemes...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Ankit Pandy, Saroj Kumar Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

Wireless sensor networks play an important part in the monitoring and analysis of the physical environment since they compile data from a wide range of sensors. In this research study, a clustering protocol is presented with the intention of establishing an automated framework for the organization of a wireless sensor network (WSN). The data gathering, processing, and decision-making capabilities of the network are all improved as a result of the implementation of the suggested protocol, which also contributes to the network's increased overall efficiency. In this paper, we analyse the security concerns surrounding MANETs and suggest a few promising new lines of study. Several key and trust management systems have been created and suggested in order to prevent attacks from occurring from outside the MANET system. Additionally, a number of secure MANET routing protocols have been developed in order to prevent attacks from occurring from inside the MANET system. Concerning the detection of intrusions, research has been conducted on a recently developed intrusion detection framework that was developed with MANET in mind especially. In order to address the issues about MANET's level of security, measures of both prevention and detection will be used. The clustering technique that was suggested in this research makes use of a hierarchical, scalable network architecture that is focused on energy optimization. The strategy that has been offered reduces the amount of energy that is wasted while simultaneously increasing the effectiveness of the network in terms of data collection and transmission. This is accomplished by intelligently grouping sensor nodes into clusters according to the amounts of energy they possess and the capabilities they possess. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Routing protocols

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Improved AO based Classification Framework for Eye Diseases 2

Improved AO based Classification Framework for Eye Diseases

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saju, Binju Asha, V. Mathew, Jincy C Sangma, Sengcheng Ghosh, Shouvik Sivaguru, K. Adi Shankara Institute of Engineering & Technology Department of Computer Science - AI Engineering Kerala Kalady India New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model. © 2024 IEEE.

关键词： DCNN Diabetic retinopathy Gaussian Mixture IAO ResNet

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Explainable Artificial Intelligence in Healthcare -A Review 7

Explainable Artificial Intelligence in Healthcare -A Review

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ishwarya, S. Pillai, Anitha S. Hindustan Institute of Technology And Science Department of Computer Applications Chennai India Hindustan Institute of Technology And Science School of Computing Sciences Chennai India

ISBN: (纸本)9798350359299

This study focuses on the application of Explainable Artificial Intelligence (XAI) in Stroke, heart attack and cancer prediction, highlighting the importance of XAI. This technology helps users to clearly understand the reason behind detection, prediction and classification. XAI further enhances the capabilities of these models ensuring that their predictions are accurate but also transparent, interpretable, and actionable. This paper explores recent research and developments in XAI applications for three healthcare domains. It also examines the algorithm, datasets, and accuracy achieved, shedding light on how XAI has improved the accuracy and trustworthiness of predictive models. This study thoroughly analyses Explainable AI's effect on healthcare, delivering insightful information about how XAI is changing the field of predictive medicine and enhancing patient outcomes and healthcare decision-making. © 2024 IEEE.

关键词： Forecasting

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Ensemble model for Driver Distraction Detection 4

Ensemble model for Driver Distraction Detection

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Subbulakshmi, S. Arathy, A.K. Madhu, Praseetha Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Accidents on the road are frequently caused by distracted drivers, leading to severe injuries and fatalities. Real-time driver attention detection and mitigation can reduce accidents and boost traffic safety. In this paper, we suggest an ensemble approach for detecting driver distraction using the State Farm Driver Distraction dataset. To accomplish reliable and accurate classification, we combine NasNet-A large, ResNext-101, and EfficientNet-B0 Convolutional Neural Network (CNN) models. Various driver distraction scenarios are shown in the State Farm Driver Distraction dataset, which is a sizable collection of labeled images. The ensemble model aims to achieve higher accuracy and robustness in detecting driver distraction. The ensemble model uses fusion techniques that enhance the overall performance and reliability of the system. We evaluate the performance of our ensemble model from accuracy and loss function. Findings show that the ensemble model performs better than individual CNN models. This research contributes to enhancing road safety by providing an effective real-time system for driver distraction detection, which can be integrated into vehicles and driver assistance systems to mitigate the risks associated with distracted driving. © 2023 IEEE.

关键词： Convolutional Neural Network Driver distraction Driver distraction scenarios EfficientNet-B0 NasNet-A large ResNext-101 Road accidents

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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Adaptive Fusion Techniques for Image Super-Resolution 2

Adaptive Fusion Techniques for Image Super-Resolution

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Ga, Pranesh Vadakkan, Akshay Anto Jisha, R.C. Amrita School of Computing Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350308846

This research paper presents a thorough analysis of Image Super-Resolution (ISR) methods, covering their historical evolution, challenges, recent advances, and significance across domains. It explores both traditional interpolation-based and data-driven machine learning approaches, examining factors like image priors, network architectures, and the importance of domain-specific datasets and data augmentation. The paper also delves into ensemble models and addresses ongoing challenges in the field. It concludes with a comprehensive comparison of ISR methods, considering strengths, weaknesses, and applicability, offering a valuable resource for researchers and practitioners in the computer vision community and paving the way for future developments in image super-resolution. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) for Super Resolution Deep Learning Super Resolution Enhance Image Quality Image Reconstruction Image Super Resolution

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