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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Rubadevi, B. Suresh, Helina Rajini Abdullah, Ibrahim Choudhary, Prashant Kumar Choudhary, Minu Mathurkar, Piyush Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Al-Turath University Computer Science Department Baghdad Iraq Lovely Professional University Department of Electronics and Communication Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350372816

The limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. The study presents the Modified Artificial Hummingbird Algorithm with deep learning (MAHAdl), a significant breakthrough in pest detection. MAHAdl combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHAdl provides through data collection, preprocessing, and algorithmic integration. MAHAdl outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. The innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid CNN-lSTM Machine learning Algorithm for driver distraction detection 2

Hybrid CNN-LSTM Machine Learning Algorithm for Driver Distra...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kothuri, Sri Raman Sujay, V. Sreedhar, Siva Satya Juyal, Prachi Jyothi Prasad, M. Bhuvaneshwari, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Machilipatnam India Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India Uttarakhand Dehradun India RMD Engineering College Department of ECE Tamil Nadu Chennai India Paavai Engineering College Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

driver distraction is a critical safety concern on roadways, with potentially life-threatening consequences. The proposed system combines the spatial characteristic extraction capabilities of CNNs with the temporal modeling capabilities of lSTMs to effectively capture and analyze distractions in real-time. In the hybrid model, the CNN component processes visual input data, such as images or video frames, to extract spatial features that characterize driver behavior. These features are then fed into the lSTM component, which accounts for the temporal context and dependencies within the data. This approach promises to significantly enhance road safety by providing an intelligent system that can alert drivers and authorities to potential distractions, thereby reducing the risk of accidents. This study signifies a pivotal advancement in the progression of advanced driver assistance systems (AdAS) and driverless vehicles. The hybrid CNN-lSTM technique offers a promising route for safer and more reliable transportation systems with 93 % accuracy in detecting drivers' distractions. © 2024 IEEE.

关键词： activity recognition Advanced driver Assistance Systems distraction detection long Short-Term Memory network object detection spatial features temporal

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Topic Controlled Steganography via Graph-to-Text Generation

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computer Modeling in engineering & Sciences 2023年第7期136卷 157-176页

作者： Bowen Sun Yamin li Jun Zhang Honghong Xu Xiaoqiang Ma Ping Xia School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Yichang Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Department of CSIS Douglas CollegeNewWestminsterBCV3L 5B2Canada College of Computer and Information Technology China Three Gorges UniversityYichang443002China

Generation-based linguistic steganography is a popular research area of information *** text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention ***,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in *** this paper,we propose a topic-controlled steganography method which is guided by graph-to-text *** proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is *** also provide a graph path coding method with corresponding detailed algorithms for graph-to-text *** from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional *** test our method in different aspects and compare it with other text generative steganographic *** experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

关键词： Information hiding linguistic steganography knowledge graph topic controlled text generation

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An Efficient SFlA and CUCKOO Search Hybridization for Source distribution in Cloud Computing

An Efficient SFLA and CUCKOO Search Hybridization for Source...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Malsoru, Vankudothu Patil, Amareshwari Kusuma, S.M. Shivakanth, Gandla Samanvita, N. Cmr Technical Campus Department of It Hyderabad India Pda College of Engineering Department of Computer Science & Engineering Kalaburagi India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350398465

Cloud calculating is a computing prototype that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. It provides a pay-as-you-go model for gaining instantaneous online access to a shared pool of resources. This strategy is proposed to cut down on the required amount of computing time. In the most recent paper, authors employ a hybrid approach to resource allocation by combining two powerful optimization algorithms: the Shuffled Frog leaping Algorithm (SFlA) and the Cuckoo Search (CS) Algorithm. With this algorithm, we can get around the computational time constraints of the HABCCS algorithm and the krill herd algorithm. It's easily scalable, so even niche uses can benefit. The SFlA has the potential to generate a global optimization solution and comes with the added benefits of faster convergence and simpler implementation. Algorithms from the field of computer science are used in difficult situations because of their advantageous simplicity in evaluation. The knapsack problems that normally crop up during the phase of allocating resources are also addressed. The efficacy of the third strategy is evaluated. The investigational consequences demonstrate the greater presentation of the projected method equated to the state-of-the-art methods. © 2023 IEEE.

关键词： Cloud computing

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted Boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Gore, K.B. Chidambararaj, N. Kartheesan, l. Ghamande, Manasi Vyankatesh Sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. By implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. during the preprocessing phase, an Adaptive Inverse Normalization layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RBM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both dBN and KNN. © 2024 IEEE.

关键词： Irrigation

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deep learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

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

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, d. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting North Indian Ocean Tropical Cyclone Intensity 2

Forecasting North Indian Ocean Tropical Cyclone Intensity

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Mahajan, Akshath daru, deap Thaker, Aditya Narvekar, Meera Mukhopadhyay, debajyoti Computer Engineering D. J. Sanghvi College of Engineering Mumbai India D. J. Sanghvi College of Engineering Mumbai India WIDiCoReL Research Lab Mumbai India

ISBN: (纸本)9781665460477

India is prone to tropical cyclones annually, originating from the North Indian Ocean basin. Tropical cyclones are destructive and sudden natural occurrences that annually wreak havoc by taking a huge toll on human lives and property. This engenders a need for accurately forecasting the scale of such mass-destructive events, to provide us with enough time to take precautionary measures that can reduce the death toll and minimize costs. Using the CyINSAT dataset, which gives a multimodal and temporal resolution for TCs occurred from 2014 to 2022, this paper employs and compares multiple techniques to solve the wind speed forecasting issue. All models involve recurrent networks along with image feature extractors, which are used together to predict the next wind speeds from a sequence of images. The architectural differences between these models mainly focus on the nuances involved in handling the current wind speed. The proposed architecture gives higher importance to the currently recorded wind speeds and performs significantly better than the baseline models. It successfully obtained an RMSE of 6.31, MAE of 0.093 and MAPE of 4.53. © 2022 IEEE.

关键词： Forecasting

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Air-Writing Recognition System 1

Air-Writing Recognition System

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Ramya, S.Thanga Sakthi, R. Rohitha, B. Praveena, d. R.M.K Engineering College Computer Science and Design Chennai India R.M.K Engineering College Computer Science and Engineering Chennai India R.M.D Engineering College Information Technology Chennai India

ISBN: (纸本)9781665456876

Air-Writing is the use of an object or finger movements to create linguistic characters or words in free space. Writing in the air with fingers/using an object is practically a metaphor of pen-based writing. detecting intended writing among extraneous finger/object movements entirely irrelevant to letters or words poses a challenge which should be addressed in the common pattern recognition methods. We propose a system that writes the exact mean of the motion that is drawn in front of the sensor using Hidden Markov Algorithm and OpenCV. This system draws the exact motion and is also used for virtual key generation. The output is shown like 2d trajectory and we will recognize the word error of the exact drawing. We will also analyze the 6-dOF (degree Of Freedom) motion of the recognition. The findings of the experiments indicate that the average rate of recognition of digits and numbers is 98.3% accurate. © 2022 IEEE.

关键词： degrees of freedom (mechanics)

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deep learning Based Cancer detection in Bone Marrow using Histopathological Images

Deep Learning Based Cancer Detection in Bone Marrow using Hi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Rani B, Swaroopa Geetha, B. Shivaprasad Yadav, S.G. Shivakanth, Gandla Manjula, B.M. Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bangalore India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The cancerous proliferation of abnormal white blood cells (WBC) in the bone marrow and blood is characteristic of leukemia, which has its origins in these blood-forming tissues. Blasts are immature aberrant cells that inhibit the creation of new, lineage-normal blood cells. Acute lymphoblastic leukemia (All), Acute Myeloid leukemia (AMl), Chronic lymphocytic leukemia (Cll), and Chronic Myelomonocytic leukemia (CMl) are the four Chronic lymphocytic leukemia (Cll), Chronic myeloid leukemia (CMl), and Acute myeloid leukemia (AMl) (CMl). Patients with leukemia are often diagnosed after their symptoms have been evaluated, a complete blood count (CBC) has been run, and a peripheral blood smear has been examined under a microscope by a pathologist. As part of the process to confirm and classify leukemia, a bone marrow examination and other sophisticated laboratory testing are performed. Traditional blood and bone marrow smear examination using light microscopy is plagued by intra- and inter-observer variability. Overcoming these individual biases of doctors is possible with the use of image processing-based approaches that can automatically analyse pictures of blood and bone marrow smears to detect abnormal cells. Using an American Society of Hematology dataset, the researchers classified cases of All into B-cell and T-cell subtypes, as defined by the World Health Organization (ASH). The research improves classification accuracy to 94.12% and compares the effectiveness of three distinct training strategies. The research also includes subtyping AMl into M3, M4, and M5 using a ResNet-50 pretrained network and traditional machine learning based classifiers based on the CNN's characteristics. Using the features of ResNet-50, we were able to get a classification accuracy of 96.43 percent, and we were able to get a hundred percent accuracy using just a small number of traditional classifiers. Using the properties of Element and an ASH dataset, the researchers also sought to clas

关键词： Extraction

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A Novel Routing Protocol for UWSNs Based on Integrated Simulated Annealing and Sine Cosine Algorithm 2

A Novel Routing Protocol for UWSNs Based on Integrated Simul...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Agarwal, Rohit Saravanan, T. Mohamed Ali, Hassan Premalatha, B. Samyuktha, P. Ramesh, P.S. GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq CMR College of Engineering & Technology Department of ECE India GRIET Department of AIML India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798350368109

This paper proposes a new SA-SCA based routing protocol for UWSNs with the objective of costing the better routing paths. The presented protocol has been designed to consider the nature of UWSNs which include energy limitation, dynamic network structure and high end to end delay. Simulation outcomes revealed that the proposed SA-SCA protocol saves energy consumption up to 13% and extend the network lifetime up to 9% as well as the delivery ratio is up to 5% more than the conventional SA based protocols. The outcomes established show the ability of the protocol in improving the throughput and dependability of UWSNs. © 2024 IEEE.

关键词： Energy utilization

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