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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nithila, Ezhil E. Bibin Christopher, V. Raj, J. Relin Francis Srinivasan, A. Soundiraraj, N. Krishnan, R. Santhana Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350379945

'DensePneumoNct' is a specialized deep learning algorithm designed for accurate and efficient detection of pneumonia from CT chest images. Leveraging the DenseNet architecture within TensorFlow, this algorithm demonstrates superior performance across critical metrics including accuracy, recall, precision, and F1 score. The implementation utilizes the ImageNet dataset, preprocessed to ensure standardization and resizing, optimizing input uniformity for robust model training. The DenseNet model architecture features dense blocks and transition layers, fostering strong feature propagation and effective downsampling of feature maps. Hyperparameter tuning, including a learning rate of 0.001, a batch size of 64, and training over 100 epochs, enhances model convergence and performance refinement. Training enhancements such as early stopping and learning rate scheduling further stabilize model training and prevent overfitting. Evaluation on a validation set showcases 'DensePneumoNet''s capability to distinguish COVID-19 pneumonia, viral pneumonia, and healthy lung tissues with unparalleled accuracy. Comparative analysis against ResNet-50, Inception-v3, SqueezeNet, and LeNet confirms DensePneumoNet's superiority in all evaluated parameters, affirming its efficacy for critical medical imaging tasks. 'DensePneumoNet' represents a significant advancement in pneumonia detection, poised to enhance diagnostic accuracy and clinical decision-making in medical settings. © 2024 IEEE.

关键词： Medical imaging

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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Computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 564-576页

作者： Ramesh, Komminni Thomas, Christine Ann Osei-Asiamah, Joel Pagidipati, Bhuvaneswari Muniyandy, Elangovan Reddy, B. V. Suresh El-Ebiary, Yousef A. Baker BoS Anurag Engineering College Suryapet District Telangana Kodad India Department of English and Cultural Studies Christ University Bengaluru India Gauteng Province Pretoria South Africa West Godavari Dt Andhra Pradesh Bhimavaram534204 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Faculty of Informatics and Computing UniSZA University Malaysia

Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners’ cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for E

关键词： Feedforward neural networks

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Preserving culinary heritage and promoting sustainability: an overview of botanical nutrition regarding herbs and spices used on the territory of today’s Cameroon

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SN Social sciences 2024年第7期4卷 1-48页

作者： Dongmo, Fabrice Fabien Dongho Zangueu, Calvin Bogning Asongni, William Djeukeu Tsopgni, Wilfried Dongmo Tekapi Tchoutezou, Guy Herman Zanguim Dongmo, Jasmine Nguimfack Tchuenbou-Magaia, Fideline Laure Etame, Rebecca Madeleine Ebelle Sameza, Modeste Lambert Gouado, Inocent Ngane, Rosalie Anne Ngono Department of Biochemistry Faculty of Science University of Douala P.O. Box 24157 Douala Cameroon Department of Animal Biology and Physiology Faculty of Sciences University of Douala P.O. Box 24157 Douala Cameroon Department of Home Economic Advanced Teacher’s Training College for Technical Education University of Douala P.O. Box 1872 Douala Cameroon Department of Chemistry Faculty of Sciences University of Douala P.O. Box 24157 Douala Cameroon Department of Forestery Faculty of Agronomy and Agricultural Sciences University of Dschang P.O. Box 222 Dschang Cameroon School of Engineering Computing and Mathematical Sciences Faculty of Science and Engineering University of Wolverhampton Wulfruna Street West Midlands Wolverhampton WV1 1LY United Kingdom

Herbs and spices are not merely condiments;they are the essence of our cultural heritage, infusing our cuisine with flavors that do more than tantalize the palate, they offer a spectrum of health benefits. This study meticulously reviews 59 botanicals, 44 spices and 15 herbs, cherished in Cameroon, presenting a treasure trove of ethnobotanical and ethnonutritional wisdom. Fruits emerge as the most favored part of plants used in spices. Herbs often hail from Asia and Europe, and spices from the lush tropics and subtropics of Africa. This review casts a spotlight on the critical role these plants play in culinary artistry and health promotion, thanks to their rich array of bioactive compounds. It navigates through the complexities of sustainable management and conservation, advocating for the longevity of these botanicals for the enrichment of future generations. The paper calls for rigorous scientific research to substantiate the health claims associated with these natural wonders and promotes sustainable harvesting and market expansion. It underscores the necessity of educating the public about the value of these plants. In conclusion, the paper urges continued research into the scientific validation of health benefits, the adoption of sustainable practices, the exploration of value-added products, market development strategies, and heightened public awareness. This research aims to lay a robust foundation for future endeavors, aspiring to holistically manage health and well-being through the sustainable harnessing of these invaluable natural resources. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cameroon Culinary traditions Ethnobotany Herbs and spices Sustainable practices

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Security enhancement of LSB-based audio steganography method

Security enhancement of LSB-based audio steganography method

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Jovana Jezdimirović Nenad Pekez Jelena Kovačević Computing and Control Engineering Department Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia RT-RK Institute for Computer Based Systems LLC Novi Sad Serbia

The rapid development of digital technology has brought about the challenge of ensuring information security. Cryptography and steganography are among the various techniques available to address this challenge. These techniques come in different forms and provide reliable means of securing information and communication. Despite the existence of numerous options, new variations are still emerging. This paper concentrates on Least Significant Bit (LSB) coding, which is one of the most widely recognized and frequently utilized steganography techniques. The advantages of LSB-based coding methods are high capacity and low complexity. However, predictability is an issue. Audio files’ sizes and data redundancy make audio data perfect for steganography, as it is possible to embed large amounts of secret information and easily transmit the signal via various communication channels. However, the use of LSB coding in audio steganography has a drawback due to the extreme sensitivity of the human auditory system (HAS). This means that any noise added to the audio with data embedded in the LSBs can be detected by the HAS, which is dependent on the number of LSBs used. This work presents an improvement of the LSB-based approach, the XORing of LSBs method, with an emphasis on enhanced security. The method achieves this by randomly picking the pairs of bits to be XOR’d while preserving the characteristics of classic LSB methods. To verify the method, it has been implemented on the Cirrus Logic DSP, proving that it may be efficiently used in a real-time audio environment without interfering with HAS sensitivity levels.

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX 8

Simplifying Graph-Parallel Computation in Apache Spark with ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Vyakaranam, Lasya Raman, Ramakrishnan Shah, Harshal Mishra, Nilamadhab Meenakshi, R. Murugan, S. Pune Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX's performance, expanding its features, and integrating it with Spark's ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores. © 2024 IEEE.

关键词： Apache Spark Big Data Graph Analytics Graph-Parallel Computation GraphX

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Integrating Deep Learning in Project Management: Improving Efficiency and Effectiveness in Project Planning and Execution 4

Integrating Deep Learning in Project Management: Improving E...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Shukla, Sunil Singh, Jagendra Sahu, Hemant Kanthalia, Shilpa Taufikin Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Department of Cs & It Udaipur India Faculty of Management Studies Udaipur India Kudus Department of Postgraduate Kudus Indonesia Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

The purpose of this research is to explore the use of deep learning technologies, namely the K-Nearest Neighbors model within the textile manufacturing process to test the improvement of the said process. To properly implement the model, we must use real-time data from the process and evaluate the improvement depending on a number of factors: how much does the maintenance cost decrease, how accurate is the inventory management, and how well the program forecasts demand, all of which should lead to an improvement of profit. First, the analysis of the results proved that the implementation of KNN model leads to a significant decrease of maintenance costs: while without the model the reduction was 15%, with it cost maintenance cost a 25% decrease. Second, the usage of a model improves inventory management accuracy: before the use of KNN, it was 85%, while after it rose to 95%. Third, demand forecasting becomes more accurate, as the difference was 75% to the positive without the model and 90% with it. The use of a model improved all aspects, and the most improved was profit: before the implementation of the KNN model, profit was 10%, and it was 40% after, which led to the conclusion that the implementation of deep learning models, namely the KNN model, improves the effectiveness of textile manufacturing process and positively affects the profitability and the production process as a whole. © 2024 IEEE.

关键词： Inventory control

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sivasankar, C. Saravanan, G. Pradeepa, H. Arun, V. Ganesh, E.N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy. © 2023 IEEE.

关键词： Charging Station Dynamic Load Management Electric Vehicle Internet of Things Solar Energy

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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Computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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One Solution for Deterministic Scheduling on GPU for Automotive Algorithms

One Solution for Deterministic Scheduling on GPU for Automot...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Kenjic, Dusan Milosevic, Milena Antic, Marija Teslic, Nikola University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (纸本)9781665404174

In order for higher levels of autonomous driving to be achieved, the demanding environmental perception and decision-making algorithms are developed. Meeting real-time system requirements is a mandatory task for all of these algorithms to be properly utilized. In order to meet these requirements, GPUs are widely used, and one of the biggest challenges is providing the deterministic scheduling of different tasks on GPU based on their priority classes. The complete determinism which ensures the absolute control of processes on GPU has still not been achieved. In this paper, we propose and evaluate one method that allows scheduling of tasks with different types of time constraints. © 2021 IEEE.

关键词： Scheduling

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