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Comparative analysis on Intrusion Detection system through ML and DL Techniques: Survey

Comparative analysis on Intrusion Detection system through M...

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2021 International Conference on Computational Intelligence and Computing Applications, ICCICA 2021

作者： Sekhar, Ch Pavani, K. Rao, M Srinivasa Dept.of Computer Science and Engineering A.P Visakhapataqnam India

ISBN: (纸本)9781665420402

Daily, large amounts of data are generated. Unauthorized users should be kept away from the data. Issues and problems arose one after the other as a result of the continuous development of network security. To avoid these malicious attacks, deep learning and machine learning methodologies are frequently used. Machine learning is a branch of the computer field that studies computational algorithms to convert empirical data into usable models. This field originated from the communities of traditional statics and intelligent retrieval. Machine learning includes deep learning as a subset. A system that can be trained to recognise objects using raw input has referred to as a deep learning system. In this study, we are applying DL techniques such as CNN, DNN, LSTM and RNN on NSL-KDD dataset. In this paper, we conduct a comparative analysis of multiple algorithms to determine which model is best for network security based on the network conditions and environment. © 2021 IEEE.

关键词： Network security

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Pushing the Limits of Machine Translation with GPT-4 and Marian NMT in Multilingual Communication

Pushing the Limits of Machine Translation with GPT-4 and Mar...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Jain, Sourabh Sadim, Mohd Amhia, Hemant Kumar, N. Krishna Choudhury, Shibanjali Rajlakshmi, P.V. GGITS Dept of Computer Science & Engineering M.P. Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P. Bhopal462044 India Jabalpur Engineering College Electrical Engineering department MP Jabalpur India Guru Nanak Institutions Technical Campus Dept.of CSE Ibrahimpatam Telangana Hyderabad India Techno College of Engineering Dept. of CSE Tripura Agartala India Kongu Engineering College Department of English TN Erode India

ISBN: (纸本)9798331539948

Machine translation (MT) plays a crucial role in enabling linguistic diversity in online interactions. In this research, we provide a unique method for expanding the capabilities of MT inside a multilingual communication framework by combining GPT-4 with Marian Neural Machine Translation (NMT). We present a complete technique comprising translation quality evaluation, contextual coherence augmentation, and translation delay optimization. The results show that our method is successful in enhancing translation quality, preserving contextual coherence, and decreasing delay. Three algorithms are used in the suggested technique. 1) Contextual Coherence Enhancement, 2) Translation Latency Optimization, and 3) Translation Quality Assessment using BLEU and METEOR ratings. The first method uses BLEU and METEOR scores to quantitatively measure translation quality, guaranteeing linguistic precision. The second method improves conversational fluency by increasing contextual coherence. The third method minimizes the time it takes to translate a sentence, facilitating simultaneous translation and conversation. We evaluated our approach by comparing it to six established MT techniques. According to the outcomes, our strategy is superior to the status quo in many respects. In particular, our approach enhanced translation quality as measured by BLEU and METEOR ratings. It also had a far greater Contextual Coherence Enhancement Factor (CEF) and Coherence Improvement Percentage, indicating that it successfully preserved the rational progressions of conversations. Furthermore, significant improvements in translation latency were also highlighted by the Latency Optimization Ratio (LOR). © 2024 IEEE.

关键词： computer aided language translation

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An Integrated Approach to Enhance Legal Information Retrieval of Sri Lankan Supreme Court Verdicts 5

An Integrated Approach to Enhance Legal Information Retrieva...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayasooriya, Asel Ahamed, Akeel Bandara, Yomal Gavindya, Chamod Kasthurirathna, Dharshana Abeywardhana, Lakmini Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept.of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Legal professionals and law students often grapple with the arduous task of locating prior Supreme Court rulings for argument preparation and academic study, leading to a laborious and resource-intensive process. This study introduces an innovative approach to streamline the retrieval of legal information from Sri Lankan Supreme Court verdicts. The method centers on developing a Custom Named Entity Recognition (NER) model, boasting a remarkable accuracy exceeding 90%. This model efficiently extracts crucial legal entities from Supreme Court rulings, readily available on the Ministry of Justice's Supreme Court website. The high accuracy ensures precise entity extraction, followed by systematic organization within a dedicated database. Subsequently, a knowledge graph is formed by linking recorded legal entities, reducing information retrieval time to a mere 179 milliseconds, significantly outperforming existing methods. Moreover, a BART summarization model is crafted to generate concise, accurate, and insightful summaries of Supreme Court decisions, boasting an impressive ROGUE1 score of 85%. This approach revolutionizes legal information retrieval, delivering a user-friendly platform that enhances the identification of cases and fosters a deeper understanding, ultimately elevating the quality of legal research and practice. © 2023 IEEE.

关键词： Knowledge graph

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Ensemble Model Classifier in Hybrid CNN to Predict Diabetic Foot Ulcers

Ensemble Model Classifier in Hybrid CNN to Predict Diabetic ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anil Jyothi Athira Anjana Varshepally Akula Manogna Alakunta Sridhar D. Vivek Dept.of Computer Science Engineering B V Raju Institute of Technology Narsapur Medak Telangana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Diabetes arises due to decrease in the level of sufficient insulin produced in the human body. The prevalent issue faced by the individuals suffering from diabetes is Diabetic foot ulcer (DFU). Diabetic Feet affected around 1-4% individuals within 6.1% of diabetic population in 2023 globally. In this developing world, computer vision technologies are playing a vital role even in the field of medicine, particularly it can be beneficial in detecting and monitoring diabetes, along with its difficulties such as foot ulcers. Various models in computer vision are used for detecting the foot ulcers by image processing. Here the proposed novel hybrid model detects and monitors the diabetic foot ulcer in a proficient way. The proposed model is a combination of multiple neural network frameworks including EfficientNet as a Convolutional neural network architecture along with InceptionNet, Stacked Sparse Autoencoder (SSAE) and Sparrow Search Optimization (SSO). Every framework helps in performing different tasks in contribution for an efficient and accurate output. By analyzing diabetic foot images, EfficientNet brings out the information related to visual features and Inception-ResNet-v2 is able to capture the specific features as well as patterns from the images. Stacked Sparse Autoencoder (SSAE) can learn about the features in detail. Sparrow Search Optimization (SSO) is an optimization algorithm which can make the models work together optimally moreover, can eliminate unnecessary parameters which results in less memory requirement and makes the model smaller. The proposed model can improve the detection and management of Diabetic Foot ulcers and is expected to be 2 percentage higher than any existing models.

关键词： computer vision Visualization Computational modeling Neural networks Predictive models Diabetes Convolutional neural networks Optimization Monitoring Foot

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ResNet50 Architecture Based Dog Breed Identification Using Deep Learning

ResNet50 Architecture Based Dog Breed Identification Using D...

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作者： Selvaraj S Thangarajan R Anbukkarasi S Yuvan Prasad S Dept.of Computer Science and Design Kongu Engineering College Dept.of Information Technology Kongu Engineering College Dept.of Computer Science and Engineering Kongu Engineering College

Dog breed identification is one of the challenging task in animal *** what a dog was bred for can assist you in predicting and comprehending its *** breeds were created with a specific goal in mind:to have the desire to achieve something *** retain part of that drive as pets in our homes,and without a good outlet for that energy,it might manifest in undesirable *** a deep learning model,that can identify the breed of the dog with high accuracy,using this model one can easily identify the dog breed which makes easier for them to train the dog,so that we can't wait for the experts and no need for spending money for identifying the dog breed,using the mobile phones we can easily identify our dog breed simply using its *** paper deals with an advanced classification model was designed which identifies the breed of the dog.A training dataset consisting of more than 10000 images is used,CNN is used for the classification and the accuracy is achieved using Resnet50 architecture which is highest when compared to other models.

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Recurrent 3-D Multi-Level Visual Transformer For Joint Classification of Heterogeneous 2-d AND 3-D Radiographic Data

Recurrent 3-D Multi-Level Visual Transformer For Joint Class...

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IEEE International Conference on Image Processing

作者： Muhammad Owais Muhammad Zubair Taimur Hassan Divya Velayudhan Irfan Hussain Naoufel Werghi Dept. of Mechanical and Nuclear Engineering KUCARSDept Khalifa University UAE Faculty of IT & Computer Science University of Central Punjab Pakistan Dept.of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE Dept.of Computer Science C2PS Khalifa University UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of $96.67 \%$, F1-score of $96.88 \%$, mean average precision of $96.75 \%$, and mean average recall of $97.02 \%$. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： Visualization Solid modeling Accuracy Image processing Image retrieval Lung Manuals

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IOT and Deep Learning Assisted Smart Agriculture System for Plant Disease Identification

IOT and Deep Learning Assisted Smart Agriculture System for ...

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作者： Selvaraj S Thangarajan R Santhosh C Vikash Raj T K Dept.of Computer Science and Design Kongu Engineering College Dept.of Information Technology Kongu Engineering College Dept.of Computer Science and Engineering Kongu Engineering College

Agriculture employed more than 50% of labours and contributed nearly 18% to country's *** is the leading producers of Many crops,such as wheat,rice,lentils,cotton,peanuts and perishable products but in recent years,the percentage of people getting into agriculture has been *** disease and incorrect maintenance of humidity and moisture is the major reason that makes new farmers *** a deep learning model,that can identify the crop disease with high accuracy,and using this model one can easily identify the crop disease which makes easier for them treat the crop with pesticide,so that we can't wait for the *** the IOT architecture to find the humidity and soil moisture of the field so that the farmer can maintain the correct level of humidity and soil moisture through irrigation,sometimes extreme high and very low moisture and humidity leads to *** enhanced classification model was suggested in this paper to classify leaf *** with more than 10000images is used for image *** accuracy is achieved using AlexNet model and it came out to be the highest in comparison to other models,and IOT part using the Raspberry pi 3 kit with soil moisture and humidity and soil moisture sensor.

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A Machine Learning and Deep Learning Based Approach to Detect Inaccurate Health Information in Bengali Language

A Machine Learning and Deep Learning Based Approach to Detec...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Nusrat Taki Eshatur Showan Umratul Chowdhury Farzana Tasnim dept.of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

The spread of false health news and its dis-semination on the internet has become a major concern, due to its potential to have disastrous effects. To detect it, numerous approaches have been attempted. However, we are aware of very few studies that have sought to identify health related false information in Bangla. In this study, we have analyzed the performance of various Machine Learning and Deep Learning approaches in detecting Bangla health-related misinformation that is available online. We have created a comprehensive data repository, consisting more than 5000 data, manually annotated to two fixed categories. Several supervised machine learning classifiers and Deep Learning algorithms have been employed in this experiment to detect fake health news at the textual level. Our experiment achieves maximum accuracy of 88% in the Passive Aggressive approach and 89% in the Bi-LSTM approach. We believe that our dataset is a significant collection of health-related data in Bangla. It may open up new perspectives for the analysis of Bangla-language and health misinformation detection.

关键词： Deep learning Machine learning algorithms Social networking (online) Text categorization Predictive models Classification algorithms Fake news

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An Ensemble Technique to Analyse Mental Health of Workforce in the Corporate Sector

An Ensemble Technique to Analyse Mental Health of Workforce ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Abhinav Lohia Abhinav Ranjith Abhinav Sachdeva Aruna Bhat Dept.of Computer Science and Engineering Delhi Technological University New Delhi India

The modern world is filled with stress, worry, and pressure, among other things. People can’t perform at their highest level as a result, which lowers economic production and raises stress levels. This study examines the working conditions for employees physical and emotional wellbeing. It is examined whether a patient should be treated of his mental illness or not according to the values obtained in the dataset such as self-employment, previous mental health conditions in the employee’s family, whether the organisation is providing advantageous life benefits, is the employee currently undergoing any treatment for mental condition etc by applying various Machine Learning techniques. The “Open Sourcing Mental Illness (OSMI) Mental Health dataset” was used in this study. This data is used to train the proposed model using several techniques, such as Linear Regression, KNN, Decision Trees and Random Forest to find the factors each person is targeted for. Ensemble Technique is applied for increasing the accuracy of the results. The main purpose of this data analysis is to raise general awareness of workplace issues connected to mental health, which will assist to decrease the number of persons who experience mental diseases. This gives an approximate idea of how employees in both the technical as well as non-technical organisations are impacted. Whether or not mental health is taken into account and whether a person needs any medical assistance or not.

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A Solid Waste Management System Using Smart Bins in a Decentralized Manner in Ethereum Blockchain Network for Incentivization

A Solid Waste Management System Using Smart Bins in a Decent...

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International Conference on Computational Systems and Communications (ICCSC)

作者： Amitha K A Raja Varma Pamba Dept.of Computer Science and Engineering LBS Institute of Technology for Women Thiruvananthapuram India

Waste generation in the world is raising in a huge way that the requirement to devise new solutions is crucial. The Solid Waste Management, SWM finds it difficult to handle manually these waste generated day by day, as a result of urbanization. With the advent of the technologies, it is possible to find some really good solutions for the waste segregation, waste collection, tracking, and documenting on the entire process. In this paper, we propose an automated solid waste management system by bringing the IoT and block chain technologies together. Here the data related to the bin is collected and is passed to the blockchain smart contracts that can automate incentives. The system enables waste segregation, which will ease the further procedures in SWM. Smart contract is deployed in the public blockchain Ethereum network platform and the automated reward based on the weight and type of the waste through an API is proposed in this paper.

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