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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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Virtual Twin Analytics: Advancing IoT Security through AI-Powered Software Informatics 1

Virtual Twin Analytics: Advancing IoT Security through AI-Po...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

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Hybrid approaches for handling imbalanced structured and unstructured data

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Multimedia Tools and Applications 2024年 1-23页

作者： Akshay, M. Manu, Rishabh Raj, Rishika Suhas, K.T. Shobha, K. Department of Computer Science and Engineering Siddaganga Institute of Technology B.H Road Karnataka Tumkur572102 India

With their limitless potential for highly accurate decision-making behaviors, machine learning algorithms have emerged to influence the world of information systems. Algorithms designed for structured and unstructured data sets do not produce good results if the data set has an uneven distribution of classes (imbalanced), this is because these algorithms are designed and developed to generalize from training data samples and to produce the most accessible hypothesis that fits the data. Uneven distribution of classes in the training stage gives a low precision on the minority class(es) but a high accuracy on the majority class(es) resulting in high-cost losses or even catastrophes. To overcome this inevitability, this work aims to propose hybrid balancing techniques. Proposed hybrid approaches have been empirically evaluated on both structured and unstructured imbalanced data sets. Empirical results have shown that the proposed hybrid approaches have outperformed state-of-the-art techniques by achieving improved mean recall rates of 40% and 20% on Pima diabetes and Cerebral stroke structured data sets respectively. The proposed method has also outperformed in balancing unstructured data by generating minority class data with the similarity (measured using Structural Similarity Index Measure (SSIM)) ranging between 25%-60% on MNSIT and Fashion-MNIST data sets. The resultant balanced dataset is further evaluated using conventional classification algorithms, where the outcomes on balanced dataset have shown a performance improvement of 10% compared to imbalanced data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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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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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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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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Intelligent Robotic Arm: Adaptive Collision Avoidance Using Current Fluctuation Analysis in Human-Proximity Scenarios

Intelligent Robotic Arm: Adaptive Collision Avoidance Using ...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Sarawagi, Karan Pagrotra, Ashutosh Dhiman, Hardik Talwandi, Navjot Singh Deswal, Muskan Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350388534

This research introduces an Intelligent Robotic Arm with Adaptive Collision Avoidance, employing real-time current fluctuation analysis for human-proximity detection. Precision and versatility in motors and actuators are given top priority in the robotic arm setup. Motor power lines are continually monitored by integrated high-precision current sensors, and a real-time data processing system separates natural fluctuations from those caused by outside influences. When a human comes into contact with the surface of the robotic arm, the system excels at detecting their closeness by identifying unique patterns in the variations of current. This sets off an adaptive collision avoidance system, which quickly flips the main power supply's kill switch to bring the vehicle to an emergency stop. A crucial component is adaptability, as the system dynamically modifies sensitivity in response to environmental conditions. Comparative studies show that this approach performs better than conventional static sensing techniques. Experiments under control confirm the great precision, accuracy, and low false positive rate. The use of a fail-safe technique improves system integrity by preventing false alarms and enabling recovery after an emergency. © 2024 IEEE.

关键词： Robotic arms

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Forecasting the Incidence of Neglected Tropical Diseases and Vector-Borne Diseases 6th

Forecasting the Incidence of Neglected Tropical Diseases a...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Nair, Rekha R. Babu, Tina Pavithra, K. Sharma, Shashvat Kuntappalavar, Abhishek Singh, Sukhveer Rai, Vithan A. Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9789819728381

Dengue fever is a common vector-borne sickness in tropical regions, particularly in India, Bangladesh, and Pakistan. This disease, caused by mosquitoes, affects people of all ages in more than a hundred nations throughout the world. The research looks into real-time series forecasting and analysis, applying three regression models and developing a weighted average forecasting model for infectious diseases. From 2014 to 2017, the integrated diseases monitoring program of the Indian Government provided monthly statistics on dengue cases. The data was analyzed using three regression models: support vector regression, neural network, and linear regression, with performance indicators including mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE). The study found that the proposed weighted ensemble model outperformed, with an emphasis on its ability to minimize predicting mistakes. The fundamental goal of the study, forecasting error reduction, was met thanks to the weighted ensemble model’s higher performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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