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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Upadhyay, Sachin Dwivedi, Anjali Verma, Ashutosh Tiwari, Vatsya Gla University Department of Computer Engineering & Application Mathura India Government Girls Polytechnic Computer Science and Engineering Amethi India G.L. Bajaj Group of Institutions Computer Science & Engineering Mathura India Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India

ISBN: (纸本)9781665462617

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning's core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87). © 2023 IEEE.

关键词： Support vector machines

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Classification of Nutritional Deficiencies in Cabbage Leave Using Random Forest 11

Classification of Nutritional Deficiencies in Cabbage Leave ...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Nupa Ram Shukla, Ratnesh Kumar Sengar, Alok Singh Gupta, Anurag College of Computing Science and Information Technology Computer Science and Engineering UP Moradabad India Shambhunath Institute of Engineering and Technology Computer Science and Engineering UP Prayagraj India

ISBN: (纸本)9781665487320

Now a day agriculture is very important in India since it is a growing nation. But generally the crop production attained by farmers would be much below the optimal production. It is very important to correctly detecting and identifying the crop diseases to enhance the profit of the formers and the stakeholder. The main reason for the crop production gap is due to the lack of essential soil nutrients and irrigation in the agricultural farms. To escalate the crop production, it is essential to balance the chemical elements or nutrients present in the soil with varying parameters of soil like the pH and soil moisture. Crop productivity can be increased to optimum level by efficient soil nutrient management. In case of Nutrient deficiencies, visual symptoms will appear on the leaf. This paper put forwards a method to identify the nutrient deficiencies of plants by making use of visual symptoms appearing on the leaves by Classification. Eight types of deficiencies i.e. N, P, K, Ca, B, Zn and Mg will be studied. The proposed study consists of creation and pre¬processing of a set of images consisting of nutrient deficient and healthy leaves, feature extraction and by using Random Forest performing multi class classification of nutrient deficient leaves. Evaluation of tomato leaf from the dataset focuses on recognizing the visual detection and indications of nutritional deficiencies. The proposed architecture achieves the 98.30% accuracy with the model size of 9.20 MB. © 2022 IEEE.

关键词： Nutrients

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A Crime Pattern Detection using Predictive Policing with Random Forest based on Synthetic Minority Oversampling 1

A Crime Pattern Detection using Predictive Policing with Ran...

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

作者： Jegadeesan, R. Rajani Kanth, Tatiraju V. Alsalami, Zaid Subburam, S. Annapurna, Thammishati Jyothishmathi Institute of Technology and Science Department of Computer Science and Engineering Nustulapur Karimnagar India Tvr Consulting Services Private Limited Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India Hyderabad India

ISBN: (纸本)9798350352931

In recent days, major cities have created several problems in terms of public safety and early detection of crime which is an important part to reduce the impact. In existing Crime Pattern Detection (CPD) models such as impact learning and Random Forest (RF) majorly struggles with high dimensional data. In this paper, predictive policing with Random Forest based on Synthetic Minority Oversampling (PP-RF-SMOTE) is proposed for effective CPD with reduced class imbalance by producing artificial marginal samples in order to overcome sample minority class. At first, the data from Chicago open data portal is preprocessed with one-hot encoding to convert the categorical values into binary vector representations. Then, the min-max normalization is utilized to fit the binary vector representations into a certain range. Then, RF is introduced to select the optimal features from normalized binary representations. The SMOTE technique generates synthetic minority samples to oversample them in a minority class to reduce the class imbalance in RF feature selection. At last, XGBoosting classifier is utilized to classify the crime patterns into binary labels as crime or non-crime. From the results, the proposed PP-RF-SMOTE model showed improved results in contrast to existing impact learning by providing accuracy of 90.35%, precision of 92.17%, recall of 93.78% and F1-score of 92.96% respectively. © 2024 IEEE.

关键词： Normal distribution

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A Provably Secure Identity Based Authenticated Key Agreement Protocol with Multiple PKG Compatibility for Inter-Vehicular Ad hoc Networks

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Journal of Integrated Design and Process science 2022年第1期25卷 55-77页

作者： Daniel, Renu Mary Thomas, Anitha Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Kerala India Department of Mathematics Bishop Abraham Memorial College Kerala India

Vehicular Ad hoc Networks (VANETs) facilitate the timely dissemination of road traffic information, including early warnings related to road conditions, weather updates, traffic congestion and collisions. The information exchanged in VANETs is crucial for maintaining uninterrupted traffic flow, minimizing road mishaps, expediting emergency services and analyzing potential detours in case of congestion. In order to ensure the legitimacy of the interchanged messages and to preserve confidentiality, it is essential to establish secure authenticated channels among the communicating automotive nodes. Considering the highly dynamic nature of VANETs and the diversity of the participating vehicular nodes, Identity based Authenticated Key Agreement (ID-AKA) protocol in the multiple Private Key Generator (PKG) setting, is an ideal cryptographic technique for securing inter-vehicular communications. We cryptanalyze the existing pairing-free multiple PKG compatible ID-AKA (mPKG-ID-AKA) protocol designed for securing VANETs and prove that the protocol lacks weak Perfect Forward Secrecy (wPFS). Our investigations also reveal that previous attempts to model mPKG-ID-AKA protocols are computation-intensive and provide weaker security guarantees. To this end, we propose an efficient eCK secure mPKG-ID-AKA protocol based on the gap Diffie-Hellman assumption. Experimental analysis suggests that the proposed protocol attains the highest computation efficiency, when compared to the existing mPKG-ID-AKA schemes and is therefore highly suitable for securing inter-vehicular communications. © 2021 - IOS Press. All rights reserved.

关键词： Vehicular ad hoc networks

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Efficient Canine Vision: Accurate Dog Breed Classification with EfficientNet 2

Efficient Canine Vision: Accurate Dog Breed Classification w...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Godwin James, T. Prabahar Poonguzhali, C. Selvakumaran, S. Malathi, G. Kaliappan, S. Venu, Nookala Department of Computer Science and Engineering Sri Sairam Institute of Technology India Department of Electronics and Communication Engineering S.A. Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Saravanampatti Coimbatore India Madhya Pradesh Gwalior India

ISBN: (纸本)9798350384277

Understanding and classifying dog breeds is a fascinating yet challenging task due to the vast diversity within the canine species. In this project, we present a novel approach to dog breed classification utilizing the state-of-the-art Efficient Net architecture. Leveraging a comprehensive dataset comprising 133 distinct breeds, our method aims to accurately identify and categorize each breed with high precision. Efficient Net's innovative scaling method allows us to strike a balance between model complexity and computational efficiency, enabling robust classification performance even with limited computational resources. Through meticulous preprocessing techniques and data augmentation strategies, we enhance the model's ability to generalize across diverse breed characteristics, minimizing the risk of overfitting. Our experimental results demonstrate the efficacy of the proposed approach, achieving impressive classification accuracy on the challenging dog breed dataset. We conduct extensive evaluations and comparisons with existing methodologies, showcasing the superiority of Efficient Net in handling complex classification tasks with large-scale image datasets. Furthermore, we provide insights into the interpretability of the model's predictions, shedding light on the learned representations and key features contributing to breed classification. This project not only advances the state-of-the-art in dog breed classification but also contributes to our understanding of deep learning techniques applied to animal recognition tasks. In sum up, our work highlights the potential of leveraging advanced neural network architectures like Efficient Net for comprehensive and accurate dog breed classification, paving the way for applications in veterinary medicine, pet care, and animal welfare. © 2024 IEEE.

关键词： Veterinary medicine

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Optimizing City Transit: IoT and Gradient Boosting Algorithms for Accurate Bus Arrival Predictions 2

Optimizing City Transit: IoT and Gradient Boosting Algorithm...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Shanthi, S. Maruthu Kannan, B. Giriprasad, S. Babuji, R. Sivakumar, S. Malathi, N. Care College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tiruchirappalli India Sphoorthy Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Sustainable cities and people's well-being depend on efficient and reliable public transportation networks. To improve the reliability of bus arrival forecasts, we offer a new method that uses Internet of Things (IoT) data and advanced Gradient Boosting Machine learning algorithms to combine the two. Using real-time information from IoT sensors installed on buses and at strategic nodes along their routes, it constructs a comprehensive dataset that accounts for many variables contributing to bus transit delays, such as traffic, weather, and passenger load. Then, it creates superior prediction models than conventional approaches. It uses Gradient Boosting, an effective ensemble learning methodology. The findings will lead to a more dependable and efficient public transportation system by improving the accuracy of predicted bus arrival times. It helps in the continuous struggle over urban problems with mobility and sets the path for more data-driven, efficient improvement of public transportation. © 2024 IEEE.

关键词： Bus Arrival Predictions Public Transit Efficiency Public Transportation Real-time Data Smart Cities

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Biometric Aided Intelligent Security System Built using Internet of Things 2

Biometric Aided Intelligent Security System Built using Inte...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Josphineleela, R. Lekha, D. Natrayan, L. Purohit, Kamlesh Chandra Panimalar Engineering College Department of Computer Science Engineering Poonamallee600123 India R.M.K College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal601206 India Saveetha School of Engineering Simats Department of Mechanical Engineering 602107 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

Protection is critical for information, property, and even living beings. The usage of IoT and biometrics for security is becoming increasingly common as technology progresses. Facial recognition is widely accepted as a universally applicable, aggregated, and easily accessible biometric technology. Facial biometrics employ the same indicators that humans use to differentiate between persons. This study has investigated the face recognition technology in order to create a dependable door lock system. The biometric face recognition system developed using ESP32-CAM. When a face comes in front of the camera, the system instantly detects and recognizes it. A prototype was created and tested to evaluate the security functionality, effectiveness, and dependability. The findings indicate the result may be utilized effectively improve person's existence and safety, as well as for industrial and residential applications. © 2023 IEEE.

关键词： Biometrics

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Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification 2

Advancing Medical Imaging: A Focus on Efficient Net for Brai...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shashidhar, R. Manasa, R. Megha, K.M. Priyanga, P. Manjunath, A.S. Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Bengaluru India Jss Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798350372892

In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes - glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively. © 2024 IEEE.

关键词： Magnetic resonance imaging

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From Sparsity to Accuracy: Enhancing Recommendation Systems with Item-Based Collaborative Filtering and SVD Matrix Factorization 4th

From Sparsity to Accuracy: Enhancing Recommendation Systems ...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Banerjee, Arkajit Sinha, Bam Bahadur Department of Statistics Acharya Prafulla Chandra College Kolkata New Barrackpore India Computer Science and Engineering National Institute of Technology Sikkim Ravangla India

ISBN: (纸本)9789819757022

An investigation was undertaken to assess the effectiveness of item-based collaborative filtering (IBCF) within recommendation systems, particularly in scenarios where user preferences are significantly influenced by item attributes. The Pearson Correlation served as the chosen similarity metric in the User-Item Interaction Matrix. The efficiency of the recommender engine was adversely affected by a notable matrix sparsity of 93.7%, a computation based on the MovieLens 100K dataset. To mitigate this challenge, the study implemented the Singular Value Decomposition Matrix Factorization technique. After sparsity reduction and missing value imputation within the MovieLens 100K dataset, recommendations were regenerated. Notably, the proposed model demonstrated superior performance compared to the prior sparsity-based model. The efficiency increased from 75% pre-sparsity reduction to a noteworthy 79% following the implementation of the SVD algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

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computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim Mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.Alzahrani Mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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