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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepthi, Gannamani Anusha Bamini, A.M. Praveen, Yenumala Joseph Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350375190

This study explores the transformative potential of image classification algorithms like VGG16, ResNet, and DenseNet, for the early detection of pancreatic tumors using medical imaging. One of the main causes of cancer-related deaths globally is pancreatic cancer. However, pancreatic cancer detected at early stages can be cured. This study navigates through the evolution of CNNs in medical image analysis and their specific applications in pancreatic cancer detection. Pancreatic cancer poses a significant global health challenge due to its high mortality rates, often attributed to late-stage detection. The primary objective is to enhance diagnostic accuracy and enable timely intervention. The obtained results demonstrated promising accuracy levels for each model. VGG16 achieved an accuracy of 96%, ResNet demonstrated an accuracy of 98.31%, and DenseNet showcased an accuracy of 99%. Precision values for VGG16, ResNet, and DenseNet were 95%, 96%, and 95.2%, respectively. Recall values were 98% for VGG16, 95% for ResNet, and 97% for DenseNet. The F1 score for 98% for VGG16, 97% for ResNet, and 98.1% for DenseNet. The study concludes that VGG16, ResNet, and DenseNet are valuable tools for pancreatic tumor detection, with each architecture exhibiting unique advantages. By aiding in the early detection of pancreatic cancer, the suggested models may enhance patient outcomes. The research provides insights into the comparative performance of these deep learning architectures, guiding future developments in medical image analysis. © 2024 IEEE.

关键词： Image classification

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IoT Network Intrusion Detection with Deep Learning and Voice Alerts 5

IoT Network Intrusion Detection with Deep Learning and Voice...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dharshiniya, S. Daniel Madan Raja, S. School of Computer Science and Technology Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The growing complexity and scale of Internet of Things (IoT) networks have made them a prime target for cyber-attacks, necessitating the creation of Intrusion Detection Systems (IDS) to secure confidential data. A key challenge in existing IDS models is that a high loss value can indicate inadequate detection capabilities, potentially leading to missed attacks especially in time-sensitive environments. This study proposes a robust IDS framework by integrating an Autoencoder for feature extraction with Temporal Fusion Transformer (TFT) for attack detection of network intrusion in IoT environments. UNSW-NB15 dataset, was used to assess the model's efficiency. Data preprocessing techniques, including data cleaning and the applying the SMOTE, were employed to ensure balanced dataset representation. The Autoencoder's encoding layer reduces dimensionality to capture essential features and extracted that feature and that as an input to models like the TFT. The Temporal Fusion Transformer (TFT) leverages LSTM encoding, temporal attention, and Gated Residual Networks to capture complex time-series dependencies with the extracted features which help to detect attacks like Fuzzers, Dos, Backdoor, Exploits, Generic, Reconnaissance, Shellcode, Worms. After detecting the attacks, the voice alert is automatically sent to the admin which helps to maintain the robustness. The accuracy achieved by the proposed IDS is 99.06%, with a recall of 98.66%, precision of 91.59%, F1-score of 94.27% and a loss of 0.030 when compared to other traditional algorithms. These results demonstrate the model's effectiveness in identifying various types of cyber-attacks while maintaining a low loss when compared to others. The integration of the voice alert system further enhances the security of the IDS by providing immediate notifications of detected intrusion, thereby facilitating timely responses. © 2024 IEEE.

关键词： Network intrusion

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Anomaly Detection in IoV Can Bus Traffic Using Variational Autoencoder-LSTM with Attention Mechanism 5

Anomaly Detection in IoV Can Bus Traffic Using Variational A...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Pradeep, C. Malar Dhas, Julia Punitha Isravel, Deva Priya School of Computer Science and Technology Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9798331518097

The rapid growth of the Internet of Vehicles (IoV) greatly enhances the communication and data exchange capability between vehicles, which allows the real-time transmission of critical information. The CAN bus is the backbone developed to transmit data between Electronic Control Units in the in-vehicle networks and it is prone to various cyberattacks that can breach its security and integrity. To identify cyberattacks on a CAN bus this paper proposes a deep learning model employing a Variational Autoencoder in conjunction with Long Short-Term Memory (LSTM) that features an integrated attention mechanism. The proposed model leverages the temporal characteristics of CAN bus traffic to detect anomalies by identifying the differences in the reconstruction loss. The CICIoV2024 dataset, containing benign and malicious messages for the CAN bus, was used for the proposed model, which achieved high effectiveness with the accuracy of 99.78%, precision of 99.81%, a recall of 99.32%, and F1 score of 99.56%. The results show that the model is strong in accurately detecting unusual behaviour and offers a flexible solution for improving cybersecurity in IoV systems. © 2024 IEEE.

关键词： Buses

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Light People:*** Dai,*** Lo,and *** Su-innovators in silicon photonics

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Light(Science & Applications) 2024年第12期13卷 2880-2885页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.

关键词： interview offering photonics

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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

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Light(Science & Applications) 2024年第4期13卷 602-608页

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:Science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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An intelligent impulsive noise mitigation with deep learning method

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International Journal of Mechanical System Dynamics 2024年第3期4卷 346-360页

作者： Guo Yang Yuwen Qian Zikun Wang Xiangwei Zhou Wen Wu The School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjingChina The Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLouisianaUSA

To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart grid are ***,in this paper,we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method,where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time *** detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme,we develop an intelligent algorithm based on the long short-term memory *** prevent the useful signals from being eliminated as IN signals,we propose the accelerated proximal gradient method(APGM)based on tone reservation to reduce the peak-to-average power ratio(PAPR)for the transmitting signals with low computational *** addition,the closed-form expression of the bit error rate(BER)is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the *** results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation *** addition,the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

关键词： impulsive noise deep learning smart grid

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of computer Science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Smart Waste Bins using DCNN and Internet of Things 2

Smart Waste Bins using DCNN and Internet of Things

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Ande, Aaron Sundar, G. Naveen Thomas, Rino Nair, Varun Narmadha Karunya Institute of Technology and Sciences Division of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350328202

Waste sorting poses significant challenges because of several factors, including a lack of awareness and education about proper disposal, inadequate infrastructure and collection systems, cultural practices that discourage segregation, economic factors without financial incentives, confusion due to the absence of clear guidelines and regulations for waste sorting, and time and convenience constraints. These challenges give rise to various health issues. Improper waste disposal can result in the spread of diseases through pests and rodents. Burning or burying mixed waste releases pollutants that can lead to respiratory problems. Hazardous waste, when not correctly separated, can contaminate groundwater, and unsorted waste may pose injury risks for waste workers and scavengers, including sharp objects and hazardous materials. In our study, we present a smart waste segregation system and describe the development of an intelligent waste bin designed to categorize deposited waste into various groups according to its nature, spanning from household-level waste to municipal-level waste.. This smart waste bin employs Deep Convolutional Neural Networks (DCNN) to sort the waste and incorporates ultrasonic sensors, moisture sensors, and rotary actuators. This integration enables the efficient sorting and segregation of waste placed into the smart waste bin. Combining the DCNN with actuators and sensors enhances the effectiveness of waste sorting. The implementation of these sensors will be carried out using Arduino UNO R3 and the ESP32 Camera Module © 2024 IEEE.

关键词： Actuators

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Machine Learning Based Hybrid System for Rainfall Prediction and Crop Recommendation

Machine Learning Based Hybrid System for Rainfall Prediction...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Praveen, Yenumala Joseph Isravel, Deva Priya Dhas, Julia Punitha Malar Deepthi, Gannamani Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

Accurate rainfall prediction plays an important role in agricultural decision making like irrigation, crop selection, and yield expectations. Powerful machine learning models, such as regression and classification algorithms are used to analyze historical rainfall data and other relevant weather parameters to predict future rainfall patterns. This paper proposes a machine learning-driven system to empower farmers with accurate rainfall predictions and data-driven crop recommendations. A novel system that incorporates Random Forest and Adaboost models is used to predict the rainfall and recommend crops. The algorithm uses various weather parameters like temperature, wind speed, humidity and dew to predict future rainfall. Beyond rainfall, the system also recommends suitable crops based on factors like soil type, climate conditions, and historical crop performance in the region. The machine learning models are trained and validated using an open-source dataset of past rainfall records, weather data, and crop yields. The system's effectiveness is evaluated using rigorous metrics like accuracy, precision, recall and prediction error metrics, ensuring reliable predictions and recommendations. These predictions enable the farmers in optimizing resource allocation, reducing water wastage, and increasing crop productivity. © 2024 IEEE.

关键词： Rain

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