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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Deshmukh, Sheetal Vishal Thiyagasundaram, T. Sivakumar, K. Tilak Babu, S.B.G. Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Pallavaram Chennai India Bharati Vidyapeeth Yashwantrao Mohite Institute of Management Department of Computer Applications Maharashtra Karad India Sri Sairam Siddha Medical College Department of NoiNaadal Chennai India Saveetha University Simats School of Engineering Department of Mathematics Chennai Tamilnadu Thandalam India Aditya Engineering College Department of ECE Surampalem India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

VR screening uses computers to find large databases of chemical compounds and predict their potential as medications based on factors such as binding affinity, pharmacokinetics, and toxicity. This accelerates the process of discovering novel medications by concentrating on compounds with a higher likelihood of efficacy. This results in decreased expenses and increased effectiveness over an extended period. The study demonstrates the utilization of many artificial intelligence techniques in virtual screening, including deep learning networks, machine learning models, and other sophisticated computer technologies. The analysis examines the training of AI models on diverse datasets, incorporating data from existing databases, research publications, and experimental studies. This demonstrates the potential of AI in discovering novel chemicals for the treatment of cancer, infectious diseases, neurological disorders, and uncommon ailments. There are limitations associated with utilizing Molecular structure-based CADD methods. This research reviews current tools, applications, and methods for medication production speed and cost reduction. Structure-Based Virtual Screening (SBVS) is essential to medication development, according to research. © 2024 IEEE.

关键词： Diseases

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Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning 4

Artificial Intelligence driven Deep Learning for Competitive...

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

作者： Shukla, Sunil Singh, Jagendra Nassa, Vinay Kumar Saba, Masarath Bhatia, Jitesh Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Ggsi University Tecnia Institute of Advanced Studies Dept. of Information Communication Technology Delhi India Hyderabad India Epsilon Department of Client Engagement MA United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment. © 2024 IEEE.

关键词： Contrastive Learning

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New Intrusion Detection System to Protect MANET Networks Employing Machine Learning Techniques

New Intrusion Detection System to Protect MANET Networks Emp...

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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Khalifa, Marwa Mohammed Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Computer Engineering / Information Technologies Turkey University of Altinbaş School of Science and Engineering Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%. © 2021 IEEE.

关键词： Support vector machines

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using Machine Learning

Advancements in Object Detection for Accurate and Efficient ...

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International Conference on Advances in Information Technology (ICAIT)

作者： F. Mary Harin Fernandez A. Ganesh Ramachandran S. K. Saravanan M. Bhanumathi M. Sangeetha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Computer Engineering Department University of Technology and Applied Sciences Nizwa Sultanate of Oman

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations.

关键词： Visualization Technological innovation Attention mechanisms Accuracy Surveillance Transfer learning Object detection

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： A. Ganesh Ramachandran S.K. Saravanan M. Bhanumathi M. Sangeetha F. Mary Harin Fernandez Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering Saveetha School of Engineering SIMATS BigData and Network Security Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Computer Engineering Department University of Technology and Applied Sciences Nizwa Sultanate of Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression; In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to $\because \neg \neg \neg$ not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves.

关键词： Productivity computer vision Accuracy Food security Data integration Soil Predictive models Prediction algorithms Data models Crop yield

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Blockchain-Enabled Federated Learning with Neuromorphic Edge Devices for Drone Identification and Flight Mode Detection

Blockchain-Enabled Federated Learning with Neuromorphic Edge...

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Conference on Digital Avionics Systems (DASC)

作者： Alex Henderson Chris Yakopcic Jamison Colter Steven Harbour Tarek Taha Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH Department of Electrical and Computer Engineering University of Dayton Dayton OH

Unmanned aerial vehicles (UAVs), also known as drones, are expected to play an integral role in next-generation wireless avionics networks. To help secure these networks from malicious activity, machine learning (ML) based approaches have been proposed to identify drones and their flight modes via direct radio frequency signals. These approaches frequently rely on cloudcentric methods for training, which present serious concerns, such as privacy leakage, resource burden, and undesirable latency. In response to these concerns, a distributed ML paradigm known as federated learning (FL) has been proposed that enables multiple drones to collaboratively train ML models by only exchanging model parameters and not the raw data itself. Unfortunately, the conventional FL framework is strongly dependent on a centralized aggregation server with multiple resource-constrained edge devices, making it susceptible to poisoning attacks and poor network utilization. Recently, it has been found that blockchain technology (BT) holds significant promise for securing and storing data in edge applications with high levels of trust. However, the expanding chain of security blocks consumes a significant amount of computing power, thus limiting its scalability. To bridge the gap between computational efficiency and security, we propose a blockchain-empowered and energy-efficient FL framework with neuromorphic edge devices. To assess the effectiveness of our proposed framework we perform a drone identification and flight mode detection task with a spiking neural network (SNN). Finally, we compare our neuromorphic approach with competitive alternatives to validate the energy and performance gains of our system.

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GAN-Assisted Secret Key Generation Against Eavesdropping In Dynamic Indoor LiFi Networks

GAN-Assisted Secret Key Generation Against Eavesdropping In ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Elmahedi Mahalal Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Department of Computer Science Tennessee Technological University Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London Canada

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

This paper explores the vulnerability of wireless secret key generation (WSKG) to eavesdropping in a dynamic indoor light-fidelity (LiFi) network. It analyzes the channel impulse response (CIR) similarities of two moving user equipments (UEs) across scenarios with two, four, and eight UEs. We observe that as the number of UEs increases, the similarity in CIR also rises, due to the proximal movement patterns among UEs. Specifically, the similarity rate peaks at 70% when eight UEs enter the room; it then drops to 24% during the wandering phase and rises again to 80% as UEs exit the room. Consequently, an eavesdropper among the eight UEs is able to generate 27% of a legitimate UE’s secret key, it significantly reduces the key’s complexity, decreasing the number of possible keys that need to be tested to break the encryption and making it easier to predict the remainder of the key. To mitigate this issue, we introduce a novel approach that utilizes a generative adversarial network (GAN) to artificially manipulate the CIR, thereby reducing the effectiveness of eavesdropping by adding noise into the observed CIR. This method effectively reduces the CIR similarity to a negligible 1%, thus ensuring the integrity of WSKG against eavesdropping threats.

关键词： Wireless communication Vehicular and wireless technologies Scalability Noise Light fidelity Generative adversarial networks Complexity theory Channel impulse response Vehicle dynamics Eavesdropping

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GANscan:continuous scanning microscopy using deep learning deblurring

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Light(Science & Applications) 2022年第10期11卷 2378-2387页

作者： Michael John Fanous Gabriel Popescu Quantitative Light Imaging Laboratory Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA

Most whole slide imaging(WSI)systems today rely on the"stop-and-stare"approach,where,at each field of view,the scanning stage is brought to a complete stop before the camera snaps a *** procedure ensures that each image is free of motion blur,which comes at the expense of long acquisition *** order to speed up the acquisition process,especially for large scanning areas,such as pathology slides,we developed an acquisition method in which the data is acquired continuously while the stage is moving at high *** generative adversarial networks(GANs),we demonstrate this ultra-fast imaging approach,referred to as GANscan,which restores sharp images from motion blurred *** allows us to complete image acquisitions at 30x the throughput of stop-and-stare *** method is implemented on a Zeiss Axio Observer Z1 microscope,requires no specialized hardware,and accomplishes successful reconstructions at stage speeds of up to 5000 μm/*** validate the proposed method by imaging H&E stained tissue *** method not only retrieves crisp images from fast,continuous scans,but also adjusts for defocusing that occurs during scanning within+/-5 μ*** a consumer GPU,the inference runs at<20 ms/image.

关键词： hardware continuous image

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Machine Learning based Mobile Applications for Autonomous Fertilizer Suggestion 4

Machine Learning based Mobile Applications for Autonomous Fe...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Raviraja, S. Raghavender, K.V. Sunagar, Prashant Ragavapriya, R.K. Kumar, M Jogendra Bharath, V.G. University of Garden City Faculty of Computer Studies Khartoum Sudan Karnataka Mysuru India Department of Computer Science and Engineering Telangana Hyderabad India M S Ramaiah Institute of Technology Department of Civil Engineering Karnataka Bengaluru India Sri Ramakrishna Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Brindavan College of Engineering Department of Mechanical Engineering Karnataka Bengaluru560063 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

To ensure food security, advanced agriculture and the Green Movement haveincreased overall output, but with a yield gap. However, the use of agrochemicals such as fertilizers and pesticides, as well as modern cultivars, irrigation, and other inputs, continues to suffer agriculture in some locations. The agriculture-based on traditional agrochemicals may result in soil and environmental degradation, acidification, and a reduction in soil fertility. Only by utilizing appropriate fertilizers and limiting the number of soil resources that are exploited, can help to maintain soil quality and crop yield. As a result, fertilizer recommendations in agriculture are crucial to meeting productivity, and financial needs, and minimizing nutrient losses to the environment. In this research, the smartphone app will be used to construct an autonomous fertilizer suggestion system for farmers. The dataset was gathered from the website Kaggle. The encoding technique is done to make the data entries numeric. Three Machine Learning (ML) models like K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT) are employed to predict fertilizer based on environmental and soil characteristics. The best model is identified using performance metrics and deployed on the mobile app. From the performance analysis, the RF gives high accuracy of 90%. © 2022 IEEE.

关键词： Fertilizers

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WIP: Collaborating to Introduce Second-Life Battery Technology Research in an Informal STEM Learning Environment 54

WIP: Collaborating to Introduce Second-Life Battery Technolo...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Whiteside, Hope Lakin, Joni M. Combs, Dominic Davidson, Shannon Forrest, Bernadette Qahouq, Jaber Abu Alabama Transportation Institute The University of Alabama TuscaloosaAL United States The University of Alabama Department of Educational Studies TuscaloosaAL United States The University of Alabama Department of Curriculum & Instruction TuscaloosaAL United States The University of Alabama Department of Educational Leadership TuscaloosaAL United States The University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL United States

ISBN: (纸本)9798350351507

This work in progress, innovative practice, paper outlines an interdisciplinary collaborative model utilized by a team of educational researchers, and engineers to design a series of community-centered, place-based learning activities suitable for a middle or high school informal STEM learning environment. Based on the potential applications and benefits of Second-Life Electric Vehicle Batteries, the activities connect cutting-edge STEM research with multidimensional real-world challenges within their communities related to climate change, renewable energy integration and storage, intelligent transportation systems, and optimized energy management, that empowers students to think critically about the role they can play in shaping a more resilient, environmentally sustainable community. © 2024 IEEE.

关键词： Collaborative learning

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