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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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Big Data Analytics-based Intelligent Logistic System 4

Big Data Analytics-based Intelligent Logistic System

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sankar, S. Shadaksharappa, B. Ganesh Kumar, N. Padmanaban, K. Sri Sairam Engineering College Department of Management Studies Tamil Nadu Chennai India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Deemed to Be University Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350300093

Due to Internet and e-commerce data growth, logistics management uses big data. Big data's key elements and technological components are explored, as well as domestic and international research findings, data mining, and data warehouse technologies. In firm logistics management, a logistics center is chosen to use a time series model to estimate transportation freight volume utilizing big data technologies. Big data-based intelligent logistics will improve productivity, cost, and user experience, according to this article. Big data technology may help logistics businesses manage, decide, engage, and allocate resources. Logistics 4.0 uses Industry 4.0 to handle massive volumes of data between equipment, goods, vehicles, and personnel. 5G Mobile Communication Networks might address these needs and boost Smart Logistics growth. This study identifies the primary logistical sectors and activities where 5G may be deployed, stressing its projected benefits and supporting technology for large-scale adoption. The literature review also highlights 5G logistics adoption barriers, suggesting more research. © 2023 IEEE.

关键词： Big data

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Performance Evaluation of Diverse ML Techniques in Disasters Twitter classification 2

Performance Evaluation of Diverse ML Techniques in Disasters...

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

作者： Nath, Basudev Sahoo, Deepak Biswal, Bhramara Bar Patra, Sudhansu Shekhar Ray, Rakesh Kumar Sri Sri University Faculty of Emerging Technologies Cuttack India Giet University Dept. of Computer Science & Engineering Gunpur India Kiit Deemed to be University School of Computer Applications Bhubaneswar India Centurian University of Technology and Management Mca Department Khordha India

ISBN: (纸本)9798350372892

Social media has grown to be an essential and significant aspect of our lives in the modern day. Due to the widespread usage of laptops, mobile phones, and other devices, individuals find it simpler to communicate with one another and to post their opinions, concerns, and other information on social media sites like Facebook, Instagram, and tweeter. Several government and commercial organizations use these social media platforms to gather text and assess people's sentiments during times of crisis. Tracking the tweets relating to the accident is too tough for them to do. To categories tweets as 'disaster' or 'non-disaster,' the paper implemented three models, the Naïve-Bayes, KNN and support vector machine classification models. The Naïve-Bayes algorithm shows the highest accuracy of 86.591. © 2024 IEEE.

关键词： Tweets

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Fuzzy Logic System-Based Decision Systems for Proactive Cybersecurity Risk management 6th

Fuzzy Logic System-Based Decision Systems for Proactive Cybe...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Mahadik, Ramchandra Vasant Khetani, Vinit Afaq, Ahmar Pradhan, Sagar Kolekar, V.K. Pawar, Rohit Maharashtra Pune India Cybrix Technologies Maharashtra Nagpur India Pune India Department of Computer Science and Engineering Arya College of Engineering Jaipur India Department of Computer Engineering VIIT Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819619801

Proactive risk management is crucial in the ever-changing field of cybersecurity to prevent advanced threats. This study presents an innovative approach to improve cybersecurity decision-making systems by combining fuzzy logic and Random Forest, resulting in an exceptional accuracy of 98.33%. The hybrid approach aims to tackle the challenges of uncertainty and imprecision in cybersecurity data by offering a clear and strong decision-making framework. The beginning of this paper highlights the increasing cybersecurity threats and the shortcomings of conventional risk management methods. The integration of advanced decision systems is crucial as the paradigm moves toward proactive strategies. Fuzzy logic, known for its capability to manage imprecise data, is combined with the ensemble learning strength of Random Forest to form a thorough and efficient solution. The novel hybrid approach starts by converting input features into fuzzy values to represent the inherent uncertainty in cybersecurity data. A fuzzy inference system is used to convert fuzzy inputs into precise decisions, which serve as the basis for training the Random Forest model. The hybrid system effectively utilizes the ensemble features of Random Forest to identify intricate patterns in the data, resulting in an impressive accuracy of 98.33%. This research enhances cybersecurity decision systems and highlights the interpretability of the hybrid model. The system combines the clarity of fuzzy logic with the strength of Random Forest to provide a transparent view of decision-making processes and maintain a high level of predictive precision. The results highlight the efficacy of the hybrid approach in proactive cybersecurity risk management, showing its ability to greatly enhance organizational defenses against changing cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk management

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

An Involuntary Liver Cancer Recognition in Abdominal Liver I...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar, M. Vinodh Kumar Patra, Raj Veena, R. Siva Kumar Reddy, A. Sunil Kumar, V. Inventuriz Labs Pvt Ltd Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed. © 2023 IEEE.

关键词： Medical imaging

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VHASR: A Multimodal Speech Recognition System With Vision Hotwords

VHASR: A Multimodal Speech Recognition System With Vision Ho...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hu, Jiliang Li, Zuchao Wang, Ping Ai, Haojun Zhang, Lefei Zhao, Hai Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9798891761643

The image-based multimodal automatic speech recognition (ASR) model enhances speech recognition performance by incorporating audio-related image. However, some works suggest that introducing image information to model does not help improving ASR performance. In this paper, we propose a novel approach effectively utilizing audio-related image information and set up VHASR, a multimodal speech recognition system that uses vision as hotwords to strengthen the model's speech recognition capability. Our system utilizes a dual-stream architecture, which firstly transcribes the text on the two streams separately, and then combines the outputs. We evaluate the proposed model on four datasets: Flickr8k, ADE20k, COCO, and OpenImages. The experimental results show that VHASR can effectively utilize key information in images to enhance the model's speech recognition ability. Its performance not only surpasses unimodal ASR, but also achieves SOTA among existing image-based multimodal ASR. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Towards Autonomous Intrusion Detection: Leveraging Artificial Intelligence 15

Towards Autonomous Intrusion Detection: Leveraging Artificia...

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Rex, M.Jenolin Chattopadhyay, Saumitra Kumar, K. Sree Shukla, Rishi Prakash Manwal, Manika Julee Srm Institute of Science and Technology Department of Computing Technologies kattankulathur India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Jaipuriya Institute of Management Jaipur India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India Centre of excellence Chandigarh University India

ISBN: (纸本)9798350370249

The growth of cyber-attacks and records breaches, there is a growing want for effective and efficient intrusion detection structures (IDS). Conventional IDS are based heavily on human intervention and manual analysis, making them time-consuming and prone to human errors. The latest improvements in artificial intelligence (AI) have shown promising consequences in various domains, including cyber security. AI-based totally techniques, which include machine studying, deep learning, and herbal language processing, can automate the detection and prevention of intrusions in real time. In this paper, we propose a framework for building an independent intrusion detection gadget by leveraging AI techniques. The proposed framework will utilize machine-gaining knowledge of algorithms to analyze community traffic and discover any strange behavior or patterns that might indicate an intrusion. Furthermore, we are able to gain deep knowledge of fashions to research and adapt to new assault kinds and patterns constantly. © 2024 IEEE.

关键词： Conventional Intervention Intrusion Patterns Prevention

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Real-Time Anomaly Detection in Grinding Wheels Using a Multimodal Deep Learning Framework

Real-Time Anomaly Detection in Grinding Wheels Using a Multi...

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2024 International Conference on Information Technology, Data science, and Optimization, I-DO 2024

作者： Zhang, Qiaoyun Yang, Wan-Chi Chang, Hsiang-Chuan Ho, Chia-Ling Chang, Chih-Yung Chuzhou University School of Computer and Information Engineering China National Taipei University of Nursing and Health Sciences General Education Center Taiwan Tamkang University Department of Transportation Management Taiwan Tamkang University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798400709180

In the manufacturing process involving grinding wheels, challenges arise in fine-tuning grinding machines, typically addressed by craftsmen through subjective observations of sparks and sounds. This paper introduces a novel mechanism comprising two pivotal phases aimed at optimizing grinding wheel production line efficiency and accuracy. Firstly, an AutoEncoder is employed for spectrogram denoising, effectively isolating grinding sounds from environmental noise. Convolutional Neural Networks (CNNs) in the Encoder extract features across time and frequency domains, while deconvolution in the Decoder gradually restores features. ReLU activation ensures computational efficiency and effectively handles nonlinear features. Secondly, an AI-based assessment determines parameter adjustments using a combination of 3DCNN and CNN. By integrating classification results from both networks, features from video and audio data are identified, thereby enhancing classification effectiveness. Anomalies during grinding operations are detected through combined outputs, indicating the need for parameter adjustments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Anomaly detection

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Leakage-Resilient Anonymous Multi-Receiver Outsourced Revocable Certificateless Encryption 5

Leakage-Resilient Anonymous Multi-Receiver Outsourced Revoca...

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5th International Conference on computer Communication and the Internet, ICCCI 2023

作者： Tseng, Yuh-Min Chien, Hung-Yu Hung, Ruo-Wei Tsai, Tung-Tso National Changhua University of Education Department of Mathematics Changhua Taiwan National Chinan University Department of Information Management Nantou Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung Taiwan

ISBN: (纸本)9798350326956

To reduce the complexity of managing certificates in public-key infrastructure (PKI)-based public key systems (PKI-PKS) and solve the key escrow issue in identity (ID)-based public-key systems (ID-PKS), certificateless public-key systems (CL-PKS) are proposed and play an important role in public-key cryptography. But, a CL-PKS setting should consider the revocation issue to revoke intruded members so that a revocable certificateless public-key setting (RCL-PKS) is used to resolve this issue. Recently, side-channel attacks have been realized and endangered the existing cryptographic schemes/protocols. Fortunately, the research of leakage-resilient cryptographic schemes/protocols resisting such attacks have attracted the attention of cryptologists. Indeed, several leakage-resilient certificateless encryption (LR-CLE) schemes have been proposed. However, these LR-CLE schemes have some deficiencies. In the paper, the first leakage-resilient anonymous multi-receiver outsourced revocable certificateless encryption (LRAMR-ORCLE) scheme is proposed. As compared with several previous schemes, the proposed scheme possesses several merits: (1) The revocation functionality is performed by an outsourced revocation authority;(2) It guards against side-channel attacks and possesses leakage-resilient property;(3) An authorized receiver's identity is anonymous for other receivers;(4) It is a secure one-to-many encryption scheme. © 2023 IEEE.

关键词： Side channel attack

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