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Developing a Robust Intrusion Detection System Using SMOTE and Hybrid SVNN Model

Developing a Robust Intrusion Detection System Using SMOTE a...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： B. Kiranmayee M. Sharmila Devi Kalva Susheela Raghu Dhumpati Kiran Kumar Reddy Penubaka U Ganesh Naidu Department of CSE (AI&ML) G Pulla Reddy Engineering College Kurnool India Department of CSE Santhiram Engineering College Nandyal India Department of Computer Science Sir C R Reddy College for Women Eluru India Department of CSE Bahrain Polytechnic College Bahrain Department of CSE-AIML MLR Institute of Technology Hyderabad India B V Raju Institute of Technology Medak Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper proposes an integrated approach to build up IDS with proper effectiveness toward the rising need for strong network security. Network traffic anomaly detection and classification are one of the major aims and enhance the security layer against various types of cyber threats. This study is a methodical approach in which a diverse set of data is first extracted from Kaggle. The collected dataset is a comprehensive one that includes various kinds of network traffic data. The first step includes preprocessing the data, i.e., handling missing values, removing erroneous entries, and dealing with outliers using the Interquartile Range (IQR) method. To counter class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is utilized in generating synthetic samples in underrepresented classes for generalization of models. The feature selection algorithm relies on the Chi-Square Lasso Feature Selection method. This method unites Chi-Square tests to select statistically significant features with Lasso regularization for feature dimension reduction with the goal of overfitting minimization. For building models, a hybrid of Support Vector Machine and K-Nearest Neighbors, which is termed as SVNN, is used. By using soft voting to aggregate the outputs of SVM and KNN, the proposed SVNN model improves the prediction accuracy, the degree of robustness, and adaptability. The presented methodology has achieved high classification accuracy 93.28%, precision 93.49%, recall 93.28%, F1 score 93.23%, and low RMSE of 0.2592. These metrics demonstrate that the model is reliable and of practical use for intrusion detection in dynamic, high-dimensional environments of networks, providing a proper solution to modern security network challenges.

关键词： Support vector machines Adaptation models Analytical models Accuracy Intrusion detection Telecommunication traffic Nearest neighbor methods Network security Feature extraction Data models

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Machine Learning Techniques for Protein Structure Prediction in Bioinformatics

Machine Learning Techniques for Protein Structure Prediction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Abdiqayumovich, Aliyev Nurillo Jaisudha, J. Al-Farouni, Mohammed Khare, Neha Kaur, Gurmandeep Koduru, Siva Sai Fergana Medical Institute Of Public Health Assistants Of The Department Of Biophysics And Information Technologies Fergana Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Saveetha Institute of Medical and Technical Sciences Dapartment of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets;and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, adding data augmentation and transfer learning boosts the performance of the models to a mean accuracy of 87%. Predictors Evaluation: In evaluating the predictors, the model used an accuracy of 6% and a Matthews correlation coefficient (MCC) of 0. 74. The generality and efficiency of the proposed model are tested and compared with results from other similar standard datasets. Study shows that the results of the model have higher reliability than the traditional method of the machine-learning algorithm especially in the classification of the second and third levels of varied types of proteins. The research will demonstrate the applicability and possibility of the large-scale and cost-efficient approaches to the protein structure prediction using machine learning, while emphasizing the drawbacks of experimental tools. Recommendations for future work are to enhance the interpretability of the resulting models, better combine methods based on the use of information from hybrid schemes with physical models, and use more extensive databases for enhancing accuracy. This research highlights the profound impact that machine learning can have in the field of bioinformatics as a tool to enhance knowledge of such protein structures and to further practice improvements in the understanding of life processes and a and drug design. © 2024 IEEE.

关键词： Federated learning

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Optimizing Purchase Intention Prediction in E-Commerce

Optimizing Purchase Intention Prediction in E-Commerce

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Women in Computing (InCoWoCo), International Conference for

作者： S. Jayanthi Rajeshwari D Naresh Goud M R. Geetha S. Benjamin Franklin Pannangi Rajyalakshmi Department of Artificial Intelligence &Data Science Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education (IFHE) Telangana India Department of CSE Sri Indu Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India Department of CSE (AI&ML) Nagarjuna College of Engineering and Technology Bengaluru India Department of Social Work Faculty of Behavioural and Social Sciences SRIHER (DU) Chennai India Department of CSE TKR College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This study investigates the effectiveness of deep learning models—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM—in predicting purchase intention on e-commerce platforms using session-level data. The dataset, sourced from the UCI Machine Learning Repository, comprises 12,330 sessions with features such as ‘PageValues,’ ‘BounceRates,’ ‘ExitRates,’ and user behavior attributes like ‘Month’ and ‘VisitorType.’ After preprocessing categorical variables using Label Encoding and standardizing numerical features, the data was split into an 80-20% training and testing set. Each of the five models was then trained and assessed using metrics such as accuracy, ROC-AUC, and other classification measures. Among the models, the CNN achieved the highest performance with an accuracy of 90.34% and a ROC-AUC score of 0.9156, followed by the LSTM model with an accuracy of 89.72% and a ROC-AUC score of 0.9102. Although the RNN model performed well, it showed relatively lower metrics with an accuracy of 88.40% and a ROC-AUC of 0.8934. ‘PageValues’ exhibited the strongest positive correlation with purchase intention (Pearson correlation: 0.4926, Spearman correlation: 0.6264), while ‘ExitRates’ and ‘BounceRates’ had significant negative correlations of -0.2071 and -0.1507, respectively. This study identifies CNN and LSTM as robust models for predicting purchase intentions in e-commerce, with ‘PageValues’ being the most influential predictor. The results provide valuable insights for optimizing e-commerce platforms to improve conversion rates. Future research will focus on external validation and expanding the dataset to ensure broader applicability across various e-commerce contexts.

关键词： Measurement Deep learning Accuracy Recurrent neural networks Correlation Predictive models Data models Numerical models Electronic commerce Long short term memory

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Dynamic Malware Classification and Signature Generation Using Multi-View Convolutional Neural Networks

Dynamic Malware Classification and Signature Generation Usin...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Sweety Prasanna Kiruba G Shubha G. Sanu K. Saranya Tatiraju.V. Rajani Kanth Mahesh S Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering (AI & ML) KLS Gogte Institute of Technology Afliated to Visvesvaraya Technological University Belagavi India Department of Electronics and Communication Engineering Sona College of Technology Salem India TVR Consulting Services Private Limited Hyderabad India Department of Computer Science and Engineering-AI Mar Baselios Christian College of Engineering and Technology Peermade Idukki

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing sophistication of malware has posed significant challenges to cybersecurity, necessitating advanced approaches for malware detection and signature generation. This study presents a novel method for dynamic malware classification and signature generation using Multi-View Convolutional Neural Networks (MVCNNs). The proposed framework captures multiple perspectives of malware behavior, including system calls, memory access patterns, and network activities, to create a comprehensive representation of malicious activities. The MVCNN architecture leverages these diverse views to enhance classification accuracy, addressing the limitations of single-view analysis. The dynamic execution of malware in a sandboxed environment generates behavior logs, which are preprocessed and transformed into multi-dimensional feature maps for input into the MVCNN. The model then extracts high-level features across multiple views, enabling robust classification and the automated generation of unique malware signatures. Experimental results demonstrate the proposed method's effectiveness, achieving a classification accuracy of over 98% on benchmark datasets. Additionally, the generated signatures exhibit high precision and recall in identifying malware variants. The proposed approach not only improves malware detection rates but also provides scalability and adaptability for real-time cybersecurity applications. This research highlights the potential of multi-view deep learning frameworks to advance dynamic malware analysis and enhance cybersecurity resilience.

关键词： Deep learning Accuracy Scalability Prevention and mitigation Benchmark testing Feature extraction Malware Real-time systems Convolutional neural networks computer security

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Strategical Implementation of Autonomous Vehicle Navigation System

Strategical Implementation of Autonomous Vehicle Navigation ...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Srikanta Kumar Mohapatra Farida A. Ali Prakash Kumar Sarangi Premananda Sahu Jayashree Mohanty Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Faculty of Engineering & Technology Siksha ‘O’ Anusandhan (Deemed to be) University Odisha India Department Of CSE (AI&ML) Vardhaman College of Engineering and Technology Hyderabad India School of Computer science and Engineering. Lovely professional University Punjab India Dept. of computer Science and Engineering IIIT Bhubaneswar Odisha India

Accidents involving motor vehicles are on the increase as our culture grows more technologically advanced. Even with mobile phones, in-car entertainment systems, higher traffic, and improved road infrastructure, the majority of traffic accidents are caused by human error on the part of the driver. Accidents caused by humans may be reduced by technology, which would be beneficial to road safety. The introduction of driverless automobiles may result in increased road safety. Everyone in the car is putting in a lot of effort to achieve this goal. Automakers Audi, BMW, Ford, Google, General Motors, Tesla, and Volkswagen, together with Volvo, are doing research and development on completely autonomous vehicles. The “Waymo” product from Google is well-known. This task involved the creation of a miniature prototype of an autonomous vehicle based on an Arduino Uno. Present study able to determine the path by graphing the starting point and ending point on Google Maps. From the Kml route, *** is able to extract both the latitude and longitude coordinates. Following that, the coordinates were entered into the code for the Arduino uno. Arduino Uno is used along with AGPS sensors to track the realtime movement along with the car's latitude and longitude coordinates and then upload them to the things peak cloud. AGPS sensors are necessary for today's smartphones. The Arduino Uno establishes a connection to the Things peak cloud by means of the esp8266 Wi-Fi module. It then retrieves the most recent data and computes the angle and distance at which our autonomous car should travel based on the latitudes and longitudes of the gmap route that have been stored in an array.

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Exploring Decentralized Identity Verification Systems Using Blockchain Technology: Opportunities and Challenges

Exploring Decentralized Identity Verification Systems Using ...

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Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Sumalatha A Desidi Narsimha Reddy B. Shamreen Ahamed Udayakumar K Arthi Priyadharshini M Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid advancement of digital technologies has underscored the critical necessity for robust and reliable identity verification systems in today's interconnected world. Traditional centralized identity management systems encounter myriad challenges, from vulnerabilities to data breaches and privacy concerns, to inefficiencies inherent in verification processes. This paper delves into the transformative potential of decentralized identity verification systems using blockchain technology as a robust solution to these pressing issues. By harnessing blockchain's immutable, transparent, and secure characteristics, decentralized identity systems not only promise enhanced privacy, security, and user control over personal data but also foster a paradigm shift towards more efficient and user-centric identity management *** study rigorously examines the foundational principles of blockchain technology and its strategic application in decentralized identity verification. It highlights key opportunities such as strengthened security measures through cryptographic protocols, enhanced user autonomy in managing and sharing personal information, and streamlined verification processes that reduce operational complexities and costs. Additionally, the paper critically evaluates significant challenges that must be addressed, including scalability concerns as blockchain networks grow, complexities in navigating regulatory landscapes designed for centralized systems, and overcoming barriers to widespread user adoption. Through an exhaustive analysis of existing models, real-world case studies, and emerging trends, this paper aims to provide comprehensive insights into the feasibility and future prospects of decentralized identity verification systems. By contributing to the ongoing discourse on innovative identity management solutions in the digital age, this research seeks to pave the way for more secure, efficient, and equitable digital ecosystems.

关键词： Data privacy Navigation Scalability Pressing Market research Blockchains Complexity theory Security Reliability Identity management systems

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Utilizing VGG19 and ResNet50 Neural Network Models for Comprehensive Intelligent Learning and Analysis of Legal Precedents and Texts

Utilizing VGG19 and ResNet50 Neural Network Models for Compr...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Anubhav Mathur Jagendra Singh P. Sudheer Rakesh Kancharla Mohammed H. Al-Farouni Priyanka School of Media Bennett University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department CSE (AI&ML) CVR College of Engineering Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram India The Islamic University Najaf Iraq University Institute of Computing Chandigarh University Gharuan India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Analysis of legal text presents its unique challenges compared to textual data due to diverse and largely unstructured content and the sophistication of legal language. In the present study, we investigate the suitability of deep learning for this challenging domain by predicting legal precedent based on helical text and image data with the help of several state-of-the-art deep learning models such as Convolutional Neural Network (CNN), VGG16, VGG19, and ResNet50. The dataset consists of a wide range of cases across various domains: civil, criminal, administrative, constitutional, and appellate domain for a total of 350 cases. The dataset is divided into 70% for training and 30% for testing. Prior to training the models, data preprocessing includes textual cleaning, tokenization, and then converting text to a numeric format using the Bag-of-Words model. Image data is resized, normalized, and augmented to increase size by 10%. The performance of the models is assessed by the standard evaluation metric including accuracy, precision, recall, and f1 score. Results indicate that the CNN model is the best performer with 96.78% accuracy followed by VGG16 with 94.5% accuracy, VGG19 with 93.2% accuracy, and ResNet50 with 90.25% accuracy. In the discussion section, we provide insights into the performance on the strengths and weaknesses of CNN along with other models. Their analysis clearly shows CNN as the most suitable model for small samples and outperforming the other models for precision, recall and F1 score. Potential use cases of this research can involve legal practice as it could greatly help lawyers carrying out precedent analysis.

关键词： Deep learning Training Analytical models Accuracy Law Predictive models Data models Numerical models Convolutional neural networks Residual neural networks

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Advanced Neural Network Approach for Fetal Heart Rate Classification with Multi-Attribute Analysis 4

Advanced Neural Network Approach for Fetal Heart Rate Classi...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Rajeswari, D. Chourasiya, Prashant Sivakumar, K.K. Sahu, Neelesh Kumar Jerith, G. Gifta Singh, Pranav Mlr Institute of Technology Department of Cse Hyderabad India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Mohan Babu University School of Liberal Arts and Sciences AP Tirupati India Gyan Ganga Institute of Technology & Sciences Dept of CSE-AIR MP Jabalpur India Malla Redy University Hyderabad School of Engineering Department of Ai & Ml Telangana Hyderabad India Ggits MP India

ISBN: (纸本)9798331518981

Electronic fetal monitoring is an essential diagnostic aid in pregnant women for the evaluation of fetal status and possible pathology. In this research, a sophisticated neural network model is suggested as a means for the arrangement of fetal heart rate indicators with multi attribute analysis to assist in maximizing the classification. We thus use an ANFIS that incorporates the capability of learning from the neural network on the one hand and the reasoning of fuzzy logic on the other. The system employs ECG data and the signals are processed by Independent Component Analysis method to extract the fetal signals. Further, PSD transforming of a signal and RR-interval measurements are incorporated to obtain significant signal characteristics making the discrimination more sensitive and specific. The investigational outcomes also show that the proposed model achieves a much higher overall accuracy than the traditional methods, providing an effective means for the fetal heart monitoring in practice. © 2024 IEEE.

关键词： Fetal monitoring

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Block-Chain Assisted Strategies for Power Loss Reduction in Modern Distribution Systems

Block-Chain Assisted Strategies for Power Loss Reduction in ...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICaiQSA), International Conference on

作者： Chodagam Srinivas P Sailesh Babu V Durga Rao A Venkateswara Reddy G Mallikarjuna Heman Chittapragada Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India Department of EEE SRKR Engineering College Andhra Pradesh India Department of AI&ML Swarnandhra College of Engineering and Technology Andhra Pradesh Department of EEE Sai Rajeswari Institute of Technology Proddatur Andhra Pradesh India Department of CSE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This paper presents a novel approach utilizing Golden Jack Optimization (GJO) algorithm integrated with block-chain technology for minimizing power losses and enhancing voltage profile in distribution systems (DS). The optimization framework incorporates the IEEE 69 bus system as a testbed, employing GJO to iteratively optimize capacitor placements and reactive power compensation. The backward-forward sweep technique is adopted for accurate loss calculation and voltage profile assessment. Key components of the proposed methodology include transparency, trust, decentralization, data integrity, and traceability facilitated by block-chain technology. By recording each iteration's best solution, fitness value, and capacitor placements on an immutable ledger, block-chain ensures transparency and trust among stakeholders. Moreover, the decentralized nature of block-chain prevents unauthorized modifications, enhancing data integrity. The traceability feature enables stakeholders to track the evolution of optimization results, providing insights into the optimization process. Experimental results demonstrate the effectiveness of the proposed approach in minimizing power losses and improving voltage profile, showcasing the potential of block-chain-integrated optimization algorithms for enhancing distribution system performance.

关键词： Accuracy Data integrity System performance Capacitors Voltage Recording Stakeholders Security Optimization Lifting equipment

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A Management of Food Supply Chain in Sustainable Smart Cities using Fire Hawk Optimization

A Management of Food Supply Chain in Sustainable Smart Citie...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： B Girirajan B. Raveendra Naick Hassan M. Al-Jawahry Revanasiddappa Rana Veer Samara Sihman Bharattej R Department of ECE School of Engineering SR University Warangal Telangana India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University Florida United States

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, the urban cities are facing increasing strain due to the swift increase in population inside metropolitan areas. It is anticipated that the centered strategy for smart cities will address both the ecological environment and urban life. The food business is one of major IoT application areas in smart cities. In smart cities, IoT technology aid in the real-time monitoring, analysis, and management of the food business. In this study, an Internet of Things (IoT) based Dynamical Food Chain of Supply for Smart Cities using Dynamic Vehicle Routing (IFSCDVR) using Fire Hawk Optimization (FHO) technique is suggested, which guarantees food quality while also offering intelligent vehicle routing and the ability to identify the reasons for contamination of food. This strategy would increase the effectiveness and precision of the supply chain network with the smallest possible dataset size. The findings demonstrate that the suggested system works better than the current methodologies. The proposed IFSCDVR-FHO achieved overall performance of 91.34% of accuracy, 89.56% of precision, 89.68% of recall and 90.21% of f1. score.

关键词： Accuracy Smart cities Computational modeling Supply chains Routing Data models Sensors

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