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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kodmurgi, Pruthvi S. Adiga, Srihari Parthasarthy, Srikrshna Thanushree, R. Avabratha, Varun Vishwanatha Kanwal, Preet Honnavalli, Prasad B. Pes University Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

This study proposes a multi-modal architecture to detect dark patterns, leveraging the usage of textual, visual, and temporal contexts. Dark patterns are deceptive elements used in websites and apps that manipulate users into making decisions that benefit the company, often at the expense of the user. In today's world, with a significant rise in the adoption of technology across the globe, detecting dark patterns becomes important. This study is more focused on the dark patterns in the e-commerce space. We propose using three modules to detect this task, each of which processes different types of data. For textual data, we leverage Large Language Models to detect potential dark patterns. For visual data, we use object detection models to detect any visual cues that might reveal potential dark patterns. For the temporal context, we track and analyze the states of the website. Using these three modules makes our detection process robust and versatile. Furthermore, we created an image dataset DarkPattern-Capture (DPC), which has images of potentially deceptive elements;our object detection models are trained and tested on this dataset. © 2024 IEEE.

关键词： Object detection

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Deep Learning-Based Web Application for Real-Time Apple Leaf Disease Detection and Classification 2

Deep Learning-Based Web Application for Real-Time Apple Leaf...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Satish Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350370188

Agriculture is the most significant industry in the economy of India. Various kinds of diseases affect the leaves of plants and influence the productivity of crops. Apple farmers are also constantly facing challenges in boosting their yield and protecting apple trees from diseases. The prevalence of diseases and pests significantly hampers apple production, leading to substantial financial losses for the industry each year. Farmers may not have expertise in leaf disease prediction. Detecting Apple Leaf Diseases (ALD) swiftly and accurately is crucial for effectively handling and curbing these issues within orchards. Specifically, advancements in computer vision methods utilizing Deep Learning (DL) have opened up avenues for identifying and understanding these diseases at an early stage directly on the leaves. Web application based on the DL model is proposed to address this issue, which can predict Healthy and Alternaria, Leaf Spot, Marssonina Blotch, and Powdery mildew disease of the affected leaf. © 2024 IEEE.

关键词： Forecasting

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A Reduced State Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri Nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model checking method traverses the full state space, which suffers from the state space explosion problem. A reduced state space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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A Hybrid Model Combining Vision Transformer and Generative Pre-trained Transformer for Image Captioning

A Hybrid Model Combining Vision Transformer and Generative P...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Hina Vishwesh, Jatangi Y D M V Prasada Jagadeesh, M. Sucharitha, M. Pradyothan, Vitta Sai Bennett University School of Computer Science Engineering & Technology Noida India

ISBN: (纸本)9798331508685

Creating descriptive captions for images is now becoming a mission-critical application area in the intersection of natural language processing and computer vision. This work provides the hybrid model VisionGPT2, combining Vision Transformer for encoding images and GPT2 for text generation, which enables accurate, coherent, and context-aware image descriptions. The model is tested over a dataset by using BLEU score metrics and has achieved a high BLEU score of 0.4877, which ascertains the strength of cross-modal architectures for image captioning. In this paper, the design and training process of VisionGPT2 has been discussed along with the benefits and challenges faced while proceeding and the potential applications the model has gained in performance for future use. © 2025 IEEE.

关键词： computer vision

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Smart Weed Management and their Profile Mapping using Deep Learning and Digital Mapping 2

Smart Weed Management and their Profile Mapping using Deep L...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Tiwari, Amit Kumar Singh, Digvijay Yadav, Avanish Mishra, Saurabh Singh, Saloni United Institute of Technology Computer Science & Engineering Prayagraj India

ISBN: (纸本)9798350371314

Removal of weeds has been a challenging task for farmers. Conventional method of weed removal includes indiscriminate spraying of herbicides despite the fact that the presence of the weed is patchy. To find a better solution for weed management, two prototypes were developed using low-cost hardware and open-source Deep learning models: Prototype-1, used a Raspberry Pi 4 B and YOLO v5 model while Prototype-2, used a more powerful NVIDIA GTX-1080 Ti GPU and YOLO v7 model for weed detection. Both prototypes were evaluated in custom potato fields, targeting common weeds like Chenopodium album and Portulaca oleracea while avoiding potato plants. The prototype-1, due to its low processing capabilities showed a significant lag in real-time detection of weed while the prototype-2 showed promising results with average precision of 97% and recall of 91%. A GPS device and Digital Mapping system was also integrated to calculate the effectiveness of Herbicide after evaluating the before and after results with the help of Smart Agro-Sprayer. These experiments show a promising future for the fusion of latest technologies like AI (Deep learning), Automation, Digital Mapping and Data analytics to solve conventional agriculture problems and promote precision farming to increase crop yield with sustainable model. © 2024 IEEE.

关键词： Herbicides

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Enhanced Lung Cancer Detection Using Graph Convolutional Networks (GCN) Approach 4

Enhanced Lung Cancer Detection Using Graph Convolutional Net...

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

作者： Moozhippurath, Bineesh Jayapandian, N. Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518981

Early detection of lung cancer is a critical factor in improving the survival rates of patients. In the meantime, current methods of diagnosis often do not cover the complete process as they bypass the disease's initial stages. According to the survey, it suggests a unique way to lung cancer prediction involvement of Graph Convolutional Networks (GCNs) in the acquisition of diagnostic data to raise the accuracy and exactness of diagnostic models. This approach converts patient data from a tabular format to a graph structure, with each node representing a patient and edges representing similarities between two separate clinical features. The proposed GCN model is based on the idea of a multi-layer architecture, enabling it to effectively access both local and global patterns in the data, which in turn ensures the elapse of the performance of prediction. The model was evaluated with thoroughness and it showed the potential to replace the traditional machine learning algorithms in identifying the lung cancer cases correctly at a higher precision. Our methodology unites the advantages of graph convolution networks, so that it can reach our aim of greater accuracies and precision, which gives a good recognition for our approach of early lung cancer detection. The outcomes of the experiments confirm the value of graph-based deep learning models in medical diagnostics especially when the case is about complex tasks like lung cancer prediction. This study reflects the ongoing efforts on reliable and accurate diagnostic tools with the use of AI, thus facilitating the early detection of lung cancer and increasing patient's survival. © 2024 IEEE.

关键词： Lung cancer

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EnRaFS: An Ensemble Ranking-Based Feature Selection Approach for Grading Gallbladder Cancer Using Radiomic Analysis 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Jitani, Nitya Verma, Vivek Kumar Sarmah, Rosy Department of Computer Science and Engineering Tezpur University Assam India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

Grading of gallbladder cancer (GBC) is pivotal for the diagnosis and treatment planning of patients suffering from this disease. Radiomics has emerged as a non-invasive, imperative, and efficient way for disease diagnosis and prediction with the use of machine learning approaches on medical data. Given the large dimensionality of the data, it is important to choose the most significant features to aid in improved classification of patients with respect to the subtypes/grades of GBC. This paper proposes a novel ensemble ranking-based approach called EnRaFS, for feature selection to grade GBC patients’ using CT scan images. It combines the results of multiple feature selection methods to improve the accuracy of the ranking. The ranked features are then used to train the machine learning model to predict the grade of the cancer. The proposed approach has been evaluated on a dataset of 105 patients diagnosed with GBC and compared with other state-of-the-art feature selection methods based on accuracy measure. Our study concludes that the proposed approach can be used as an effective tool for grading GBC, which can help clinicians to make more informed decisions about the treatment of the disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Grading

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Efficient Water Pliability Prediction for Human Consumption-Exploratory data analysis (EDA) and Multi Algos 15

Efficient Water Pliability Prediction for Human Consumption-...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rimi, Tanzina Afroz Readoy, Tanmoy Komer Chowdhury, Md Tahmid Noori, Sheak Rashed Haider Chakraborty, Narayan Ranjan Mojumdar, Mayen Uddin Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350370249

Water quality assessment is crucial for public health, prompting the gathering and preprocessing of a comprehensive dataset encompassing diverse quality parameters. This study focuses on enhancing the prediction of water suitability for human consumption through the utilization of exploratory data analysis (EDA) and a multi-algorithm approach. Two Kaggle water quality datasets are merged into one, and an additional class of 'Usable but Non Drinkable' is introduced. In the dataset, patterns and anomalies are identified through exploratory data analysis. The performance of several methods, including Decision Trees, Random Forests, SVM, KNN, Gradient Boosting, and a Voting Classifier, is maximized using machine learning and CNN. Each algorithm undergoes extensive training, evaluation, and tuning, and its performance is measured using a variety of metrics. Random Forests achieved the highest accuracy of 85.76%. The paper outlines algorithmic advantages and disadvantages to aid in selecting the best algorithms for predicting water pliability. The ensemble method utilized by the Voting Classifier highlights the advantages of algorithm fusion. © 2024 IEEE.

关键词： CNN Random Forest Voting Classifier Water Pliability Water Quality

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Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective 3

Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prakash, S. Jaya Devi, D. Muthukumaran, N. Romila, R. Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India Maha Barathi Engineering College Department of Computer Science and Engineering Tamilnadu Chinnasalem India A.R. Engineering College Department of Computer Science and Engineering Tamilnadu Villupuram India Sri Muthukumaran Institute of Technology Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Several vital resources are increasingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) approaches to combat intrusion detection. Existing approaches, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative approach. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable approach. © 2025 IEEE.

关键词： Network intrusion

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Secure Rotation Invariant Face Detection System for Authentication

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computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic *** rotation is a variable component and makes face detection a complex and challenging task with varied angles and *** problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been *** objective of the paper is to implement a robust method for face detection taken at various *** to achieve better results than known algorithms for face *** RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid *** MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation *** this manner,RIFDS can detect human faces at different rotations and with different facial *** RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena *** results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations face detection frontal faces facial expressions emotions

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