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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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Securing Digital Governance: A Deep learning and Blockchain Framework for Malware Detection in IoT Networks 2

Securing Digital Governance: A Deep Learning and Blockchain ...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models. © 2024 IEEE.

关键词： Decision trees

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Bird Species Classification and Prediction Using machine learning Algorithms 7

Bird Species Classification and Prediction Using Machine Lea...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Abhinav, Penchala Dhavale, Sunitha Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501286 India Defence Institute of Advanced Technology School of Computer Engg. & Mathematical Sciences Pune411025 India

ISBN: (纸本)9798331527822

The BirdClef-2024 dataset, which includes acoustic recordings of 182 Western Ghats species, was used to classify and predict bird species in this study. Three machine learning algorithms were analyzed for their accuracy, precision, recall, and F1 score - K Nearest Neighbors (KNN), Random Forest, and Support Vector machine (SVM). The Random Forest model outperformed the other models in all metrics. The paper contains sections on dataset composition, preprocessing steps, algorithm implementations as well comparisons of the model's performances. Such issues have been successfully addressed through advanced pre-processing techniques coupled with complex machine-learning algorithms despite the intricate nature of the sound signals. A Streamlit GUI that is user-friendly has been developed to facilitate real-time recognition of birds by breed;this can contribute to surveillance for environmental biodiversity conservation efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Next-Generation Intrusion Detection: Navigating Challenges and Embracing AI

Next-Generation Intrusion Detection: Navigating Challenges a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shivhare, Mani Dhir, Saru Shrivastava, Santosh Kumar Augn Amity School of Engineering and Technology Greater Noida India Amity University Dept. of Information Technology Noida India G L Bajaj Institute of Technology and Management Dept. of Artificial Intelligence and Machine Learning Greater Noida India

ISBN: (纸本)9798331508685

The emergence of networked autonomous systems has brought a new wave of security threats. Because of not be able to adopt the traditional intrusion detection approaches with the new threats, AIML based cybersecurity defences have gain more attention in recent times. The current state of AIML-based intrusion and abuse detection is examined in this paper, with a focus on significant scientific advancements and the real-world obstacles preventing its broad deployment. As cyberattacks become more sophisticated, one thing has remained the same: Intrusion Detection Systems are a must for protecting digital infrastructures, and they need to be robust. This review summarises the awareness of IDS from traditional signature-based methods to an upcoming disruptive focus on artificial intelligence, especially ML and DL. We present a comparison of the advantages and disadvantages of these techniques, emphasizing challenges such as dataset limitations, adaptability to operate in real-time and detecting unseen attacks. Forthcoming, we examine new research directions (hybrid methods, ensemble learning and challenging advanced datasets) to improve the efficacy of IDS on ever more complicated network landscapes. © 2025 IEEE.

关键词： Network intrusion

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Advances in Glaucoma Detection through machine learning technology 1

Advances in Glaucoma Detection through Machine Learning Tech...

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1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350330915

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye. © 2023 IEEE.

关键词： CNN ConvNets Glaucoma machine learning MATLAB Rim-One

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing using Modified AES 2

A Patient-Centric Blockchain Framework for Transparent and S...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. machine learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

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Smart Agriculture in the Era of Big Data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming 2

Smart Agriculture in the Era of Big Data: IoTAssisted Pest F...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Deepak Pawar, Priyanka Pramod Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HWACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption. © 2024 IEEE.

关键词： Smart agriculture

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LSTM deep learning model for Alzheimer's disease prediction based on cost-effective time series cognitive scores 5

LSTM deep learning model for Alzheimer's disease prediction ...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Saleh, Hager El-Rashidy, Nora Abuhmed, Tamer El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Egypt Sungkyunkwan University College of Computing and Informatics Korea Republic of Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350381030

Alzheimer's disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer's disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance. © 2023 IEEE.

关键词： Data fusion

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IoT Based Monitoring System for Health During Menstruation 4

IoT Based Monitoring System for Health During Menstruation

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Khadse, Chinmaya Hajare, Shital Sheikh, Nishad Dahule, Roshni Kate, Priya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Sawani Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD. © 2025 IEEE.

关键词： Electronic health record

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