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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Jha, Saurabh Sanghi, Akash Upadhyay, Pragati Agarwal, Gaurav Invertis University Department of Computer Applications Bareilly India Psit College of Higher Education Department of Computer Applications Kanpur India Invertis University Department of Computer Science and Engineering Bareilly India

ISBN: (纸本)9798331528201

The 21st century is characterized by the emergence of AI tools, which have become essential in today's diverse application landscape. These tools find utility across various sectors such as research, customer service, medical sciences, education, software industries, and factories. AI has the capability to generate images and videos that closely resemble reality, often making it challenging to distinguish between authentic and altered content. In contemporary usage, artificial intelligence software is frequently employed in creating WhatsApp videos and Instagram reels, blurring the line between reality and fabrication. Consequently, there is a tendency for such manipulated content to circulate unchecked, masquerading as genuine news. In this research paper, we aim to examine instances of ostensibly authentic news that have been tampered with using image editing tools. Accurate identification of such manipulated content is crucial, particularly as it may serve as essential legal evidence. © 2024 IEEE.

关键词： Deep fakes Exception face forensics++ face swap face2face MobileNet Neural Textures

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Object Recognition Through Content Based Feature Extraction and Classification of Sounds in IoT Environment 2nd

Object Recognition Through Content Based Feature Extraction ...

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2nd International Conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Sethi, Srinivas Rath, Manisa Kuanar, Sanjaya Kumar Sahoo, Ramesh Kumar Computer Science Engineering and Applications IGIT Sarang Dhenkanal India Computer Science Engineering GIET Gunupur India

ISBN: (纸本)9783031569975

The content-based order of urban sound classes is an important aspect of several emerging techniques and applications as a result, the research issue has gotten a lot of attention recently. The goal of this work is to develop an effective Machine Learning(ML) based approach for urban sound categorization in cities for intelligent object detection and recognition through sound for environmental study. Audio samples from urban space has been classified through various ML algorithms based on Mel spectrogram technique. Where, mel spectrogram techniques is a frequency based approach for extraction of features from audio samples for classification. The Urban Sound dataset, which contains a total of 8732 number od sound samples belongs to ten different classes, has been used in this work. This may be used for smart object detection through sound coming from objects in different applications, such as Traffic Management, Agriculture, Smart city, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Object detection

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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Prostate Cancer Prediction Using Convolutional Neural Networks 3

Prostate Cancer Prediction Using Convolutional Neural Networ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sethi, Bijaya Kumar Singh, Debabrata Rout, Saroja Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Computer Applications Bhubaneswar India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350364569

Cancer-related mortality in men is highest among men who suffer from prostate cancer. The lack of clarity and consistency of early symptoms often makes diagnosis a challenge in the later stages (stages III and IV). Existing diagnostic techniques face challenges such as subjectivity, variability between observers, and lengthy testing processes involving biomarkers, biopsies, and imaging tests. This paper introduces a novel convolutional neural network (CNN) algorithm for prostate cancer diagnosis and prediction to overcome these drawbacks. An additional dataset of histopathology images was used to train and validate the system before it was put through its learning phase. In the study, 95.12% of cancerously derived cells were identified correctly and 93.02% were identified correctly, a remarkable accuracy of 98.07%. As a result of this study, Various challenges associated with expert evaluations by humans were successfully addressed, including higher misclassification rates, interdependencies between observers, and lengthy analysis periods. Prostate cancer diagnosis and prognosis have been made much simpler and faster by this research. Moving forward, to optimize the effectiveness of our proposed method, future investigations should explore the latest developments and innovations in this field. © 2024 IEEE.

关键词： Convolutional neural networks

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Image-based rice leaf disease detection using CNN and generative adversarial network

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Neural Computing and applications 2025年第1期37卷 439-456页

作者： Ramadan, Syed Taha Yeasin Islam, Md Shafiqul Sakib, Tanjim Sharmin, Nusrat Rahman, Md. Mokhlesur Rahman, Md. Mahbubur Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this doma

关键词： Generative adversarial networks

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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A verifiable multi-secret image sharing scheme based on DNA encryption

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Multimedia Tools and applications 2025年第4期84卷 1967-1983页

作者： Chattopadhyay, Arup Kumar Saha, Sanchita Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Central Institute of Technology Kokrajhar Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret input images into n share images. This encoding ensures that if a subset of participants collude, no information about the secret images can be revealed. The secret images can only be recovered if all participants cooperate. Most of these schemes are built using basic Boolean operations, primarily XOR operations. MSIS schemes that rely on Boolean logic offer various benefits compared to other methods, such as the ability to recover data without any loss, no increase in pixel size, and efficient computation. This article presents a verifiable MSIS scheme that relies on a secure two-variable one-way function and a secure hash function. The scheme allows for independent verification of both the dealer and the participants to detect any cheating attempts. The proposed scheme utilizes the Deoxyribonucleic acid (DNA) encryption technique to leverage the inherent benefits of DNA computing, such as enhanced security, fast computation, minimal storage needs, and low power usage. The share images produced by the proposed scheme exhibit an average entropy of 7.99, indicating that the generated 8-bit share images are completely random. Additionally, the correlation coefficient is approximately 0.005, suggesting that neighboring pixels in the images are not related to each other. The proposed scheme can detect any instance of cheating, whether it is done by the dealer or by a participant. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： DNA

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Machine Learning Based Phishing Attacks Detection Using Multiple Datasets

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第5期17卷 71-83页

作者： Aljammal, Ashraf H. taamneh, Salah Qawasmeh, Ahmad Salameh, Hani Bani Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Software Engineering The Hashemite University Zarqa Jordan

Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset;the performance of the classifiers has been affected © 2023, International Journal of Interactive Mobile *** Rights Reserved.

关键词： Network security

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Apple Leaf Disease Detection Using Transfer Learning

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Annals of Data science 2025年第1期12卷 213-222页

作者： Wani, Ozair Ahmad Zahoor, Umer Shah, Syed Zubair Ahmad Khan, Rijwan Department of Computer Science & Engineering Islamic University of Science and Technology Jammu & Kashmir Awantipora India Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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