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Early SkinDiseaseIdentification Using Deep Neural Network

computer Systems science & Engineering 2023年第3期44卷 2259-2275页

作者： Vinay Gautam Naresh Kumar Trivedi Abhineet Anand Rajeev Tiwari Atef Zaguia Deepika Koundal Sachin Jain Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraIndia School of Computer Science University of Petroleum and Energy StudiesDehradun248007UttarakhandIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Department of Systemics University of Petroleum&Energy StudiesDehradunIndia ABES Institute of Technology GhaziabadIndia

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and *** disease is the most common disorder triggered by fungus,viruses,bacteria,allergies,*** diseases are most dangerous and may be the cause of serious ***,it requires to diagnose it at an earlier stage,but the diagnosis therapy itself is complex and needs advanced laser and photonic *** advance therapy involvesfinancial burden and some other ill ***,it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier *** techniques have been proposed to detect skin disease at an earlier stage but fail to get ***,the primary goal of this paper is to classify,detect and provide accurate information about skin *** paper deals with the same issue by proposing a high-performance Convolution neural network(CNN)to classify and detect skin disease at an earlier *** complete meth-odology is explained in different folds:firstly,the skin diseases images are pre-processed with processing techniques,and secondly,the important feature of the skin images are ***,the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network(DCNN).The approach proposed in this paper is simple,fast,and shows accurate results up to 98%and used to detect six different disease types.

关键词： Convolution neural network(CNN) skin disease deep learning(DL) image processing artificial intelligence(AI)

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Real-Time Anomaly Detection in Docker Containers: A Continuous Learning Approach Using SF-SOINN

Real-Time Anomaly Detection in Docker Containers: A Continuo...

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2025 Joint National Conference on Cybersecurity, ITASEC and SERICS 2025

作者： Ejeh, Dennis Glenn Foresti, Gian Luca Miculan, Marino De Nardin, Axel CYSEC IMT School for Advanced Studies Lucca Italy Department of Mathematics Computer Science and Physics University of Udine Italy

As cyber threats are becoming more sophisticated than ever with the rapid expansion of internet-connected systems and increased use of containerized environments, we present Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), a novel approach to unsupervised anomaly detection in containerized platforms. Whereas traditional Intrusion Detection Systems (IDS) utilize supervised learning that uses known attack signatures for training, SF-SOINN employs a continuous learning approach to adapt dynamically to new data patterns, thereby eliminating the need for labeled datasets. This capability enables effective real-time detection of zero-day threats in dynamic environments. SF-SOINN have demonstrated efficacy in identifying malicious attacks on the real-world NSL-KDD dataset, and we extended its application to containerized environments, using the KubAnomaly framework. Our benchmark results reveal that SF-SOINN outperforms traditional supervised models like Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and unsupervised KubAnomaly, particularly in scenarios involving complex attacks. The performance metric considered here focused on the optimization of False Positive Rate (FPR), while balancing other key performance metrics like accuracy, recall, and precision to achieve best results - and we anticipate this approach will lay a strong foundation for developing robust anomaly IDS in future. © 2025 Copyright for this paper by its authors.

关键词： Zero-day attack

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Metric Analysis for AOP and OOP Programming Paradigm

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Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 215-220页

作者： Patel, Shrikant Katiyar, Sanjay Kumar Sharma, Naveen School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science Bhai Parmanand Institute of Business Studies GGSIPU Shakarpur New Delhi India

Object-oriented programming (OOP) is a programming that is centered around the articles and their conduct's ideas. It has been utilized for a long time by programmers' gathering. Configuration examples are the best act of OOP that has been assembled. OOP centers around taking care of the issues regarding true components. In any case, sooner or later, OOP has restrictions and execution which can be improved. AOP is a programming worldview that can beat the impediment of the OOP and can accomplish the successes decidedly, yet it can likewise cause the incidental effects in the code even with the victories. Special case taking care of is the component which deals with the run time blunders to protect the customary progression of the application Nonetheless, the execution of like these instruments with viewpoint arranged programming (AOP) could prompt mistake containing situations. Utilizing the AOP idea like joint cut, cross cut, perspective and a lot more can forestall such situations. This paper attempts to carry out the code with special case taking care of with AOP ideas and distinguishes the benefits and inconveniences in gathering with the conventional OOP execution. This report gives the examination of AOP OOP through measurements investigation and gives the depicted data about this area of study. © 2022, The Institution of Engineers (India).

关键词： Aspect oriented programming

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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computer Modeling in Engineering & sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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COIN: Cost-Efficient Traffic Engineering with Various Pricing Schemes in Clouds 42

COIN: Cost-Efficient Traffic Engineering with Various Pricin...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhao, Gongming Wang, Jingzhou Xu, Hongli Yu, Zhuolong Qiao, Chunming University of Science and Technology of China School of Computer Science and Technology China University of Science and Technology of China Suzhou Institute for Advanced Research China Johns Hopkins University Department of Computer Science United States University at Buffalo Department of Computer Science and Engineering United States

ISBN: (纸本)9798350334142

The rapid growth of cloud services has brought a significant increase in inter-datacenter traffic. To transfer data among geographically distributed datacenters, cloud providers need to purchase bandwidth from ISPs. The data transferring cost has become one of the major expenses for cloud providers. Therefore, it is essential for a cloud provider to carefully allocate inter-datacenter traffic among the ISPs' links to minimize the costs. Exiting solutions mainly focus on the situations where all links adopt the same pricing scheme. However, in practice, ISPs usually provide multiple pricing schemes for their links due to market competition, which makes the existing solutions nonoptimal. Thus, a new traffic engineering approach that considers various pricing schemes is needed. This paper presents COIN, a new framework for cost-efficient traffic engineering with various pricing schemes. We propose a partition rounding traffic engineering algorithm based on linear independence analysis. The approximation factors and time complexity are formally analyzed. We further conduct large-scale simulations with real- world topologies and datasets. Extensive simulation results show that COIN can save the data transferring cost by up to 54.54% compared with the state-of-the-art solutions. © 2023 IEEE.

关键词： Cost engineering

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Fault Secured JPEG-Codec Hardware Accelerator with Piracy Detective Control using Secure Fingerprint Template 36

Fault Secured JPEG-Codec Hardware Accelerator with Piracy De...

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36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2023

作者： Chaurasia, Rahul Reddy Asireddy, Abhinav Sengupta, Anirban Indian Institute of Technology Computer Science and Engineering Indore India Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Statistical Institute Advanced Computing and Microelectronics Unit Kolkata India

ISBN: (纸本)9798350315004

This paper presents a novel methodology to design fault secured JPEG-compression-decompression (codec) hardware accelerator with piracy detective control using secure fingerprint template of intellectual property (IP) vendor. In the proposed approach, firstly, the fault secured schedule for JPEG-codec design is generated from its behavioral description. Subsequently, in order to enable detective control against piracy, secure digital template corresponding to fingerprint biometric of IP vendor is generated and implanted into the design. In order to do so, firstly, fingerprint features are transformed based on IP vendor specified key-set. Subsequently, the generated secure fingerprint template in the form of encoded hardware security constraints is implanted into the design during behavioral/ high-level synthesis phase of integrated circuit (IC) design. The implanted hardware security constraints thereby ensure the seamless detection and isolation of fake/pirated design versions during piracy detection process, before their conjunction into system on chip (SoC) of electronic systems. The proposed approach achieves the following: i) fault secured hardware accelerator for JPEG-codec with integrated piracy detective control at negligible design cost overhead ii) higher resiliency against tampering attack and iii) lower probability of coincidence (indicating definitive proof of ownership for genuine IP vendor). © 2023 IEEE.

关键词： Fault detection

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN computer science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Sharma, Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering design limitations. On the other hand, metaheuristic approaches promise to find global optima in real-world optimization problems, even in nonlinear and multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, whereas SBO finds the best cluster heads by considering communication range. By comparing the suggested approach to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster performance, simplifies cluster head selection in WSNs, and increases network efficiency overall, according to the results. To prove its efficacy in enhancing network performance, the suggested solution outperforms standalone SBO and CSA approaches across a range of performance parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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Efficient Key Management in Lightweight Cryptography: A New Hashing-Based Approach 8

Efficient Key Management in Lightweight Cryptography: A New ...

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8th International Symposium on Multidisciplinary studies and Innovative Technologies, ISMSIT 2024

作者： Al-Mahmood, Haider H. Alsaad, Saad N. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Mustansiriyah Department of Computer Science College of Science Baghdad Iraq

ISBN: (纸本)9798350354423

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system. © 2024 IEEE.

关键词： Cryptographic Algorithms Encryption Key Management Hashing Techniques IoT Security Lightweight Cryptography

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Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

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

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

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