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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. The study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. The process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. The study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. The research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP International Federation for Information Processing 2025.

关键词： Clinical research

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Truthful Online Combinatorial Auction-based Mechanisms for Task Offloading in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6488-6502页

作者： Wang, Xueyi Wang, Xingwei Wang, Chen Zeng, Rongfei Ma, Lianbo He, Qiang Huang, Min Northeastern University College of Software Liaoning Shenyang110819 China Northeastern University College of Computer Science and Engineering Liaoning Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Liaoning Shenyang110819 China Northeastern University College of Information Science and Engineering Liaoning Shenyang110819 China

Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing

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Multimedia Tools and Applications 2025年第8期84卷 4907-4943页

作者： Alqahtani, Abdullah Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University KSA Al-Kharj16278 Saudi Arabia

Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more network threats. An intrusion detection system (IDS) with low latency and high accuracy is essential in this dynamic IoT environment to identify threats. Many IDSs utilize deep learning (DL) methods, but adjusting parameters for different environments can be challenging. This work proposes an improved hybrid Deep autoencoder (DAE) and Bidirectional long short-term memory (BiLSTM) for fog item installation to protect vital networks from prompt and efficient malicious threat detection. Determining the hyperparameters of complicated DL networks is tough. Low accuracy, efficiency, and performance in high-dimensional models afflict existing approaches. Therefore, the Sparrow Search Optimization Algorithm (SSOA) is adopted to enhance model hyperparameters. Employing IoT-based data, we assess the effectiveness of our suggested model. The outcome of the experiment obtained by analyzing the suggested IDS utilizing CICIDS2017 and BoT-IoT datasets attested to their supremacy over modern systems that are currently available in terms of precision, accuracy, FAR, error rate, and detection rate (DR). To learn more about how well our model works, added two additional metrics: Cohen's Kappa coefficients and Mathew correlation. The proposed optimization model demonstrated its ability to work well with various platforms, adapt to changing requirements, and handle larger workloads. In addition, it is possible to enhance the intrusion detection rate by minimizing both the error rate and computational time. Based on the experiments conducted, the proposed method yields superior results in terms of detection accuracy and reduced latency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Exploring the potential of cloud computing in revolutionizing healthcare 4th

Exploring the potential of cloud computing in revolutionizin...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Saini, Renu Paras Tayal, Parag Thakur, Pallavi Computer Science and Engineering Department Chandigarh Engineering College Mohali Punjab Landran India

ISBN: (纸本)9781032911571

Cloud computing, which moves assets from desktop setting to cloud infra-structure—where apps are kept in a "cloud" with massive processing power and nearly infinite database storage—has fundamentally altered traditional computing over the past few years. Modern healthcare can benefit greatly from telemedicine when it is combined with cloud computing. Strategic thinking is necessary before moving healthcare assets and services to the cloud in order to weigh the advantages and disadvantages of the fresh approach, determine whether it may be implemented, and analyze its capabilities. The study illustrated how various technologies could be applied to big data analysis in healthcare using a cloud computing. In this research, we investigate the way cloud computing has changed the way in which healthcare organizations keep their data. © 2025 the Author(s).

关键词： Electronic health record

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Identification of Retinal Fundus in Diabetic Patients Using Deep Learning Algorithms

Identification of Retinal Fundus in Diabetic Patients Using ...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Sukhavasi, Vidyullatha Yeluri, Divya Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9783031513374

Diabetic retinopathy (DR) is a complication of diabetes that causes retinal changes that affect vision. If not caught early, it can lead to blindness. Unfortunately, DR is not a reversible process where treatment only controls vision. Early detection and treatment of DR can reduce the risk of blindness. Compared with a computer-aided diagnosis system, the process of manually diagnosing retinal fundus DR images by ophthalmologists is time-consuming, laborious, expensive, and prone to misdiagnosis. Deep learning has become one of the most widely used methods in recent years and achieved great success in many fields, especially in the analysis and classification of medical imaging. The most popular and efficient deep learning technique for medical image processing used is a convolutional neural network. In this project, DR color fundus photos that were examined and analyzed were used to classify and recognize objects using deep learning techniques. Diabetic retinopathy was classified using a deep learning algorithm. The prediction of diabetic retinopathy is the main topic of this essay. The CNN model is trained using training datasets, and CNN will provide the likelihood of a diabetic eye infection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Sentiment Analysis of Amazon Alexa Product Reviews: A Comprehensive Comparative Study of Learning Algorithms 5th

Sentiment Analysis of Amazon Alexa Product Reviews: A Compre...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. This work is to explore different learning algorithms to solve sentiment analysis on Amazon products. The proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. The existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Deep Learning-Based Classification of Agricultural Soil Textures for Enhanced Crop Productivity

Deep Learning-Based Classification of Agricultural Soil Text...

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International Conference on Computing and Machine Learning, CML 2024

作者： Jayashree, G. Dharshini, S. Madhu Priya Lokesh, D. Kumar, Mathan Santhiya, C. Department of Computer Science and Engineering Thiagarajar College of Engineering Thiruparankundram TamilNadu Madurai India

ISBN: (纸本)9789819778386

Accurate soil analysis is crucial for optimizing crop cultivation and management since soil quality and texture are so important in the agricultural industry. We have incorporated deep learning (DL) into agriculture for classifying different soil textures, to enable farmers to increase crop output. Convolutional Neural Networks (CNNs), a powerful DL framework, are used in our method to automatically recognize silts, stones, and other color categories by learning detailed patterns inside soil images. The implementation is leveraged in Keras, an advanced neural network interface that is powered by TensorFlow for model creation and training. We harnessed the power of the Image Generator library to efficiently manage the image dataset. We experimented with the evaluation of four CNN models: Baseline, Augment, Dropout, and Padding. The Baseline model achieved an accuracy of 82%. The Augment model, which incorporated image augmentation, delivered an accuracy of 78%, while the Dropout model, utilizing dropout layers, achieved 60%. Notably, the Padding model excelled, yielding an impressive accuracy of 90%. This study marks a significant stride in harnessing DL technology for the betterment of agriculture, offering farmers the tools they need to make informed decisions and enhance their crop yields sustainably. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Signbridge-Audio to Sign Language Translator

Signbridge-Audio to Sign Language Translator

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Shirisha, Kammadanam Deeksith, E. Mani Madhava, M. S. S Sri Surendhar, S. Karthikeyan, R. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9798331529833

The lack of communication options for Deaf and hearing people, some may say creates a significant social disadvantage in accessing the often-bare essential services. In contrast to acoustically communicated sound patterns, sign language communicates ideas freely through manual and body language. It can be used by those who have trouble speaking, by those who can hear but cannot talk, and by regular people to interact with those with hearing impairments. By automatically converting spoken words into hand gestures, this project creates a web-based interface that allows hearing-impaired individuals to communicate with normal people in real time through sign language interpretation. Two major phases of the system Speech-to-text technology interprets oral input into textual output. Next, the text is run through Natural Language Processing (NLP) algorithms leveraging the Natural language toolkit (NLTK) to be syntactically parsed in the context of sign natural rules. The last phase translates the parsed text into sign language gestures which involve hand shapes, orientation, and body movements to convey the visual meaning of a message. This system could be used to greatly reduce the communication barriers experienced by people with hearing loss and deafness using Machine Learning (ML) to continuously improve accuracy, enhance quality of life, and provide a more inclusive society between the deaf community in our daily interactions within an ever-growing world. © 2025 IEEE.

关键词： Natural language processing systems

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Comprehensive analysis of multiple machine learning techniques for rock slope failure prediction

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Journal of Rock Mechanics and Geotechnical engineering 2024年第11期16卷 4386-4398页

作者： Arsalan Mahmoodzadeh Abed Alanazi Adil Hussein Mohammed Hawkar Hashim Ibrahim Abdullah Alqahtani Shtwai Alsubai Ahmed Babeker Elhag Department of Civil Engineering University of HalabjaHalabjaKurdistan Region46018Iraq Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Communication and Computer Engineering Faculty of EngineeringCihan University-ErbilKurdistan RegionIraq Department of Civil Engineering College of EngineeringSalahaddin University-Erbil44002ErbilKurdistan RegionIraq Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61413Saudi Arabia

In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)*** models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio *** assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM *** results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models ***,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters *** is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering *** this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.

关键词： Rock slope stability Open-pit mines Machine learning(ML) Limit equilibrium method(LEM)

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