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2023 IEEE Asia-Pacific Conference on Computer science and Data engineering, CSDE 2023

作者： Evan, Md. Raisul Islam Motiur Rahman, Mohammad Tanzir Mehedi, Sk. Nazib, Abdullah Islam, Rafiqul Rahman, Ziaur Mawlana Bhashani Science and Technology University Computer Science and Engineering Tangail1902 Bangladesh Mawlana Bhashani Science and Technology University Information and Communication Technology Tangail1902 Bangladesh School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia School of Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798350341072

Intrusion Detection Systems (IDS) play a vital role in safeguarding network security by monitoring and identifying both normal and intrusive activities. However, constructing an effective IDS from extensive datasets poses a significant challenge. The process of feature selection from these datasets is crucial, as it not only enhances accuracy but also reduces the time required to build the model. In this paper, we employ a modified version of the Student Psychology-Based Optimization (SPBO) algorithm to identify the most relevant features set for IDS. Following feature selection, various machine learning algorithms are employed for multi-level classification. To evaluate our approach, we utilize the NLS-KDD dataset. Our proposed algorithm surpasses several state-of-the-art feature selection methods in terms of accuracy, precision, recall, Fl-score, and finally faster convergence. Specif-ically, our proposed bio-inspired SPBO algorithm combined with the Random Forest (RF) algorithm achieves a comparable accuracy rate of 99.81 %. To ensure better comprehensibility, we employ two widely used Explainable Artificial Intelligence (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local In-terpretable Model-Agnostic Explanations (LIME). These methods provide insightful explanations for our model's decisions, further enhancing its interpretability. © 2023 IEEE.

关键词： Feature Selection

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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

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Neural computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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Security of Medical Multimodality Images for Telemedicine Using PDESNet Encryption Standard 2

Security of Medical Multimodality Images for Telemedicine Us...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Madhuri, P. Shyamala Kumar, D. J. Nagendra Amutha, B. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Chennai603203 India Vishnu Institute of Technology Department of Information Technology Andhra Pradesh Bhimavaram534 202 India

ISBN: (纸本)9798350308846

The telemedicine approach is a famous medical assistance service in worldwide, in this process diagnosis records like Scan reports, X-ray, CT and all other radiology reports analysis has been done with doctors. Many times, sending and receiving of these images from one point to many points, due to this security has been reduced. The multimodality images are major key diagnosis elements to detect disease of patients, these multimodality images security also been reduced due to low encryption techniques. In this work an advanced cryptographic encryption technique i.e Parallel Data encryption Standard (PDESNet) algorithm. The PDES is a modern novel algorithm can provide security to multimodal images from transmission to storage part. Finally summarize the proposed algorithm with existed models in terms of PSNR, BER, NC, MSE, accuracy and recall. © 2023 IEEE.

关键词： encryption multimodal images security Telemedicine

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A Simple Moving Average-Based Predictive IoT Architecture for Energy Consumption in Smart Cities

A Simple Moving Average-Based Predictive IoT Architecture fo...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Delsi Robinsha S Amutha B Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Efficient energy management systems which monitor and forecast along with optimal consumption management are fundamental requirements for smart city development. The implementation of Internet of Things (IoT) allows real-time data collection that leads to predictive energy management prospects. This work develops an energy consumption forecast system through a lightweight IoT infrastructure which utilizes Simple Moving Average (SMA) predictive methodology. Using real-time node data from households and industries the SMA approach delivers energy consumption predictions suitable for constrained resource conditions. A model evaluation based on Mean Absolute Percentage Error and R2 scores shows the model maintains reliable accuracy despite implementation across varying consumption ranges and low computational expenses. The presented study demonstrates how SMA technology helps address smart city energy management needs and delivers an implementation plan for optimizing urban energy systems sustainably.

关键词： Energy consumption Adaptation models Accuracy Smart cities Computational modeling Computer architecture Real-time systems Internet of Things Reliability Load modeling

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A Powerful Load Balancing Algorithm for Cloud Performance Evaluation 5

A Powerful Load Balancing Algorithm for Cloud Performance Ev...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Chandar, J. Praveen Chidambaranathan, C.M. Anbarasan, Anbarasa Kumar Karthikram, A. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computers Madanapalle Institute of Technology & Science Department of Cyber Security Madanapalle India

ISBN: (纸本)9798350321425

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CRAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm. © 2023 IEEE.

关键词： Virtual machine

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Optimizing Performance in Heterogeneous Wireless Networks: Insights from Adhoc and Sensor Technologies 4

Optimizing Performance in Heterogeneous Wireless Networks: I...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Pareek, Shashank Al-Samalek, Ahmed Saleh Singh, Sandeep Al-Farouni, Mohammed Kaur, Sharanpreet Jagtap, Jayant Chandigarh University Department of Computer Science and Engineering Mohali India Al-Zahrawi University College Department of Medical Laboratory Technics Karbala Iraq School of Engineering and Technology Bahra University Department of Civil Engineering HP Solan India College of Technical Engineering The Islamic university Department of Computers Techniques Engineering Najaf Iraq Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

In this paper, we explore the implications of cloud- based platforms for agricultural data management and decision support systems, including the benefits, drawbacks, and potential developments. Cloud computing has thus played a very big role in promoting efficiency and productivity in agriculture through providing better data storage, data processing, and better decision making tools. From the review of the various platforms it was realized that the cloud solutions enhance the data management and decision support that can lead to better and faster decisions in the agricultural sector. However, there are some drawbacks, for example, data protection issues, issues of compatibility with other platforms, relatively high costs of the implementation, and the need for standardization of the data. These limitations can be considered as the factors which do not allow using the full potential of cloud-based solutions in agriculture. The study therefore recommends more research to deal with these challenges especially on the improvements in security, the integration of AI and machine learning in the system and the improvement on the costs for the smallholder farmers. Also, the problem of integrating data standards across the cloud and enhancing the compatibility of systems is of great importance to enhance the overall efficiency of cloud services. Further work in these directions is important to overcome present drawbacks and to enhance the prospects of cloud technologies in agriculture. This paper has discussed the challenges facing the agricultural sector to adopt the cloud-based platforms, thus, if these challenges are to be solved, the agricultural sector can be in a better position to improve the management of resources, sustainability and overall agricultural performance. © 2024 IEEE.

关键词： Cloud platforms

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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Computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Estimation of Clinical Parameters for Glaucoma Detection Using Image Processing and Deep Learning Techniques from the Biomedical Human Eye Images

Estimation of Clinical Parameters for Glaucoma Detection Usi...

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2024 International Conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Chandrappa, S. Dhasmana, Gulshan Naveen Kumar, H.N. Mathpati, Shilpa Gangodkar, Ninad Dept. of Computer Science and Engg. Dehradun India Dept. of Computer Science and Engneering Bengaluru India Vidyavardhaka College of Engineering Department of ECE Mysuru India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9798350376470

The technique for distinguishing the essential glaucoma is being exhibited in this segment utilizing a changed neural system with the back spread calculation ideas. The morphological administrators ideas are being utilized for the handling of the cup and disc, in the sense, the cup and the disc zones are discovered from which the proportion is registered, from where the illness can be recognition seeing the proportion. Utilizing an updated highlight extraction process, the highlights of the disc portion and the cup can be discovered, which could be utilized in the idea of cup to plate apportion (cup to disc ratio) recognition, regardless of whether the given test image is glaucomatic or not. Classification is also done using artificial neural networks (ANN) along with the training process. Simulations have been done in the Matlab environment by developing. m codes. Once the developed program is run, the simulation results are observed. From the simulation results, it can be observed that using the computed values of the cup to disc ratio, the severity of the disease can be computed (whether it is normal or moderate or severe glaucoma). Databases have been taken from the hospitals and on-line standard websites. The quantitative results are tabulated in a table. Finally, it was found that after taking a survey of 50 images, 16 were affected with normal, 16 were affected with moderate and the rest were affected with severe case of glaucoma. The Matlab results of the simulation done using coding demonstrated the profoundness with respect to the strategy proposed or developed in this exploration write-up using the concept of neural networks. © 2024 IEEE.

关键词： MATLAB

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Enhancing Bird Species Classification: A Weighted Average Transfer Learning Ensemble Approach

Enhancing Bird Species Classification: A Weighted Average Tr...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Venkata Ramana Murthy Polisetty SP Chokkalingam Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

With the escalating threat to avian life due to environmental degradation, the need for an automatic bird image recognition system has become paramount. The diversity among the estimated 11,000 bird species worldwide presents a challenge, demanding advanced technological solutions for their conservation. The proposal utilizes a custom dataset comprising 525 species to formulate a distinctive ensemble deep-learning model for categorizing bird species. The weighted average ensemble (WAE) technique, specifically combining EfficientNetB5 and VGG19, selected from a pool of five deep learning models, including MobileNetV2, ResNet152, and InceptionV3, attains an impressive 97.18 % accuracy. Using data augmentation approaches, the training data becomes more diverse, strengthening the model. The evaluation metrics extend beyond accuracy, including F1 Scores, comprehensively assessing the model's output quality.

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年 27卷 3637-3649页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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