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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs 10

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kalaiselvan, S.A. Kumar, M.Lenin Amanullah, M. Mani Chigurupati, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350353068

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO's feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH's contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments. © 2024 IEEE.

关键词： Iterative methods

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A Robust Convolutional Neural Network Architecture for Automated Pneumonia Detection from Chest X-ray Images 3

A Robust Convolutional Neural Network Architecture for Autom...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Pallavi, K. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354171

Pneumonia is a significant global health concern, especially for vulnerable populations such as children under five. Timely and accurate diagnosis is critical for effective treatment and improved patient outcomes. This project proposes a robust solution leveraging Convolutional Neural Networks (CNNs) for automated pneumonia detection from chest X-ray images. The model, trained on a meticulously preprocessed dataset of 5,216 chest X-ray images, demonstrated exceptional performance, achieving a training accuracy of 98.16% and a validation accuracy of 98.45% after 50 epochs. The architecture comprises five Conv2D layers with ReLU activation, followed by max-pooling layers, and a fully connected artificial neural network for classification. The model's success in distinguishing between normal and pneumonia-infected cases underscores its potential as a valuable tool for healthcare professionals, offering a reliable and efficient means of pneumonia identification. The model's high accuracy and efficiency make it a valuable asset, potentially reducing the workload on radiologists and enabling quicker diagnoses, ultimately leading to more timely interventions and improved patient outcomes. Comprehensive evaluation, including testing on a separate dataset and real-world image inferences, validates the robustness of the proposed solution. © 2024 IEEE.

关键词： Convolutional neural networks

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Music Recommendation system Using Facial Detection Based Emotion Analysis 4

Music Recommendation System Using Facial Detection Based Emo...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Guthula, Manvitha Sri Bordoloi, Monali School of Computer Science and Engineering Department VIT-AP University Amaravati India

ISBN: (纸本)9798350387063

Music is intrinsically important to humanity due to its significant connections with the brain. Behaviors and emotions of humans are driven by music, which generates a unique sense of unity in the brain. This paper introduces an innovative Music Recommendation system (MRS) that uses real-time face emotion detection to personalize music choices in real-time. The proposed model is based on Convolutional Neural Network (CNN) in order to recognize emotions from users' facial expressions. The emotions are then combined with a sophisticated music classification system to suggest songs that match the user's current emotional state. To enhance the user experience, user-friendly web application is created for the working of the proposed model. It allows for real-time video feed processing and instant music recommendation updates. Additionally, the system employs emotion music mapping, where musical characteristics such as pitch, tempo, and volume are analysed to correlate specific emotions with appropriate music selections. This study enhances affective computing and music information retrieval by showcasing the effective application of real-time emotion recognition in music recommendation, leading to the development of more intuitive and responsive digital media platforms. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Unmanned Aerial Vechicle Surveillance: Real Time Drone Detection with Yolov7 Deep Learning 3

Enhancing Unmanned Aerial Vechicle Surveillance: Real Time D...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Priyadharshini, M. Srinidhi, K. Murugesh, V. Department of Computer Science and Engineering Hyderabad501 203 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India

ISBN: (纸本)9798350369083

This is an advanced object detection system with YOLOv7 model, implemented for UAV surveillance as outlined in the article. By integrating the system with Roboflow serving as its database manager and data augmentation software, it benefits from a broad array of reputable datasets which are improving training and inferences. In the blog, we cover all steps from model cloning (on TensorFlow) with one/two script line conversion to preprocessing of datasets and work automation for training or evaluation. The optimizations in the implementation of YOLOv7 dog led to scalability, allow more object categories and deploy security for real time surveillance with autonomous navigation. Extensive testing demonstrates that our system exceeds state-of-the-art methods in terms of detection performance as well as efficiency, demonstrating the significant benefits for UAV hotspots gained from a deployment of advanced deep learning techniques. © 2024 IEEE.

关键词： Deep learning

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Twitter Based Emotion Recognition Using Bi-LSTM 2

Twitter Based Emotion Recognition Using Bi-LSTM

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

作者： Anbazhagan, K. Kurlekar, Supriya Brindha, T.V. Sudhish Reddy, D. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Department of Electronics and Telecommunication Engineering JSPM Narhe Technical Campus Pune India Sri Venkateshwara College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur India Rmd Engineering College Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Twitter and other social media platforms have evolved into crucial sources of information and communication for billions of people across the world. The massive amount of data collected on these platforms enables insights into people's emotions and feelings. Emotion identification, in particular, has gotten a lot of attention because of its potential uses in industries like marketing, healthcare, and social study. "Emotional states of individuals, also known as moods, are central to expressing thoughts, ideas, and opinions, which influence attitudes and behavior."In this study, we present a method for identifying the emotion or mood of a tweet and classifying it into the appropriate emotional category. The work mentioned in this paper aims to identify and analyze the mood and emotion displayed by people in their text tweets to offer ideas. We gathered tweets and replies on a particular set of themes to create a dataset with text, user, emotion, sentiment, and other data. We used the dataset to compute user impact ratings based on various user and tweet-related factors to analyze feelings and emotions in tweets and their replies. Our model used a brand-new bidirectional long short-term memory (BiLSTM) and previously learned word embedding vectors. Finally, using the latest data, we made generalized and specific consumer suggestions based on Twitter's conduct. According to the experimental findings, the suggested model has 95.56% and 99.16% prediction accuracy. Modern performance improvement was achieved for the model. © 2024 IEEE.

关键词： Tweets

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Transforming Agriculture with Cloud Computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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