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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

computer Systems Science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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Profiling and Non-Profiling Key Retrieval Attacks in Programmable Object Interfaces Using Deep Learning Cryptanalytic Techniques: A Survey 2

Profiling and Non-Profiling Key Retrieval Attacks in Program...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Maheswari, R. Krishnamurthy, M. Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Kcg College of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

Intensive Research in the area of Hardware & Networking involves Security. This key concept is bypassed in the initial Design & Development of Hardware devices (IoT), paving way for vulnerable attacks. Attacks in Hardware Devices (IoT) are broadly classified into (i) Active attacks (ii) Passive attacks. This survey focusses on Passive attacks that include side-channel analysis aimed at retrieving data or scanning open ports and network vulnerabilities. In this Technological era, Side Channel Attacks are threatening the whole world, because communication is almost digital everywhere and anywhere. Security experts and cryptologists are working on a method to prevent such attacks on cryptographic implementations or devices, in order to prevent personal information from being leaked or misused. Adapting ML/DL methodologies for providing security has been advocated in a number of researches. This paper gives an overall survey of all the Side channel attacks involving the execution of Cryptanalytic algorithms and its counter measures encountered in the last few decades. © 2024 IEEE.

关键词： Side channel attack

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A Review on Reconfigurable Antenna and its Design Methods for Sub-6 GHz Applications

A Review on Reconfigurable Antenna and its Design Methods fo...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Bhickta, Shalini Jaglan, Naveen Kumar, Amit Jaypee University of Information Technology Department of Electronics and Communication Engineering Solan Waknaghat India Jaypee University of Information Technology Department of Computer Science and Engineering Solan Waknaghat India

ISBN: (纸本)9798350364088

In modern wireless tele- communication, the key element for better and higher performance of the communication system is an Antenna. An antenna which removes all complex issues of weather, scattering and effects of multiple paths is in demand for current wireless system issues. The Antenna with Reconfiguration capabilities in current scenario solves these issues. The popularity of Reconfiguration in antennas has seen a significant growth due to its versatile capabilities. Therefore, reconfiguration in the structure of an Antenna without changing the physical dimensions and saving space realize the goal of high speed, gain, channel capacity and signal-to-Noise Ratios (SNR). In this paper a brief knowledge of various methods of designing a reconfiguration antenna is reviewed. The different switching techniques used for the activating the reconfiguration mechanism is discussed. The advancement of reconfiguration in multi-input multi-output (MIMO) systems is also studied. © 2024 IEEE.

关键词： Signal to noise ratio

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Emotion recognition with hybrid attentional multimodal fusion framework using cognitive augmentation

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Kulkarni, Shailesh Khot, S.S. Angal, Yogesh Department of Electronics and Telecommunication JSPM’S Rajarshi Shahu College of Engineering Maharashtra Tathawade Pune India Department of Electronics and Computer Engineering Sanjivani College of Engineering Maharashtra Kopargaon India Department of Electronics and Telecommunication K J College of Engineering and Management Research Maharashtra Pune India Department of Electronics and Telecommunication JSPM’S Bhivarabai Sawant Institute of Technology and Research Pune Maharashtra Wagholi India

The identification of emotions has attracted significant analysis in various sectors, including mental health evaluation and human computer interaction. A limitation of emotion detection technology is its susceptibility to misread minor emotional signals, which might results in inaccurate results. Therefore, in this paper, an adaptive social spider optimized attentional gated recurrent unit (ASSO-AGRU) method is proposed that provides an exhaustive understanding of human emotions by integrating different modalities including voice, textual data and facial expressions. The multimodal dataset tokenization, Z-score normalization, and Gaussian filter (GF) for text, audio, and video are utilized through pre-processing of data. It is followed by extraction of features for Term Frequency - Inverse Document Frequency (TF-IDF), Mel-frequency Cepstral Coefficients (MFCCs), and histogram of oriented gradients (HOG) through text, audio, and video respectively. The ASSO-AGRU based integration strategy of multimodal inputs, which captures essential time-based relationships and correlations between different modalities, results in improved emotion recognition ability. The results show that the proposed framework attains F1-score (89%), precision (87.50%), recall (88.50%) and accuracy (89.70%) thus making it a reliable and logical method for multimodal emotion identification. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adaptive social spider optimized attentional gated recurrent unit (ASSO-AGRU) Emotion recognition Multimodal fusion

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Parameterized Clustering Cleaning Approach for High-Dimensional Datasets with Class Overlap and Imbalance

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SN computer Science 2023年第5期4卷 464页

作者： Goel, Navansh Singaravelu, Mohanapriya Gupta, Shivani Namana, Sriram Singh, Richa Kumar, Ranjeet School of Computer Science Engineering Vellore Institute of Technology Chennai India Department of Computer science and Engineering Birla Institute of Technology Mesra Ranchi India School of Electronics Engineering Vellore Institute of Technology Chennai India

Software Defect Prediction (SDP) uses machine learning algorithms to detect faulty and defective modules inside software projects. Like any machine learning model, the model’s performance depends on the training data. The ongoing research in the field explores various options for improving the quality, but class overlap and imbalance hinder the models’ effectiveness. Unwarranted instances grouped with relevant data act as noise, hindering the classification process. The removal of such instances can lead to improvement in the performance of standard machine learning classification models. This paper proposes a novel parameterized clustering approach, ‘CLARANSCA‘, for cleaning high dimensional datasets suffering from class overlap and imbalance. The parameterized clustering approach ‘CLARANS’ serves as the base for the proposed method. A multi-fold approach to calculating the evaluation metrics is used, ensuring consistency in results. The aggregate results show significant improvements, especially for Recall, AUC, and G-mean values. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AUC CLARANS CLARANSCA Class imbalance Class overlap G-mean Parameterized clustering Recall Software defect prediction

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Enhancing IoT-Enabled Healthcare with Genetic-based Encryption and Authentication for Secure and Efficient wireless Data Transmission 7

Enhancing IoT-Enabled Healthcare with Genetic-based Encrypti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Padhy, Satyanarayan Vijaya Lakshmi, T.R. Ahmad, Shahnawaz Sophia, S. Senthilkumar, G. Natrayan, L. Centurion University of Technology And Management Department of Electronics And Communication Engineering Odisha India Mahatma Gandhi Institute of Technology Department of Electronics And Communication Engineering Telangana Hyderabad India Bennett University School of Computer Science Engineering And Technology Uttar Pradesh Greater Noida India Karunya Institute of Technology And Sciences Department of Computer Science And Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The Internet of Things' incorporation with healthcare systems has enabled new prospects in patient monitoring and healthcare management. This research work offers a new Internet of Things (IoT) enabled healthcare system that integrates modern sensor technology with genetically-based authentication, and encryption methods. The proposed model is designed to improve healthcare sector wireless data transfer security, and efficiency in order to allow seamless remote monitoring, and timely intervention. The proposed system offers a comprehensive real-time health monitoring protocol through integration of pressure, temperature, and electrocardiogram (ECG) sensors. Genetically-based encryption and authentication schemes are employed to address the pressing requirement for robust security in healthcare systems, and data security. Performance tests including, doctor visualization time, sensor data transmission rate, and authentication time show an overall efficiency of 98.45% proving the effectiveness of the system over a multitude of parameters. This research work presents a complete solution to real-time monitoring, and data security in secure and efficient IoT enabled healthcare systems, and makes an important addition to this field. As demonstrated through the findings, such technology can potentially revolutionize remote patient monitoring and healthcare delivery. © 2024 IEEE.

关键词： Internet of things

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A Single DC-source Asymmetrical Multilevel Inverter With Solid State Transformer With New Switching Approach

A Single DC-source Asymmetrical Multilevel Inverter With Sol...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Karunakaran, E. Suresh, Y. Rao, Bhukya Nageswar Vivek, P.S.S. Aditya, K. National Institute of Technology Department of Electrical and Electronics Engineering Karnataka Surathkal India Kolhapur Institute of Technology College of Engineering Department of Computer Science and Business Systems Kolhapur India

ISBN: (纸本)9798350309140

Cascaded H-bridge (CHB) MLI have emerged as the preferred choice due to their high quality in output waveforms with low harmonic distortion. However, a key limitation in these inverters lies in the requirement of a dedicated DC source for each bridge. To overcome this challenge, there is a transition towards asymmetrical cascaded H-bridge (ACHB) MLIs, enabling the use of a variable DC source for each bridge. The variability in DC supply is achieved through a high-frequency link. This paper introduces a novel optimized switching technique for asymmetrical cascaded H-bridge (ACHB) MLIs with a single DC input incorporating a high-frequency link (HFL). The effectiveness of the suggested approach in achieving a 27-level asymmetric multilevel inverter is substantiated by experimental outcomes obtained through MATLAB/Simulink simulations. © 2024 IEEE.

关键词： DC transformers

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Machine Learning and Deep Learning Approaches for Guava Disease Detection

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SN computer Science 2025年第4期6卷 1-28页

作者： Paramesha, K. Jalapur, Shruti Hanok, Shalini Puttegowda, Kiran Manjunatha, G. Kumara, Bharath Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Computer Science and Engineering Christ University Karnataka Bengaluru India Department of Electronics and Communication Engineering ATME College of Engineering Karnataka Mysuru India Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of CSD PES Institute of Technology and Management Karnataka Shivamogga India Department of ECE MS Ramaiah University of Applied Science (MSRUAS) Karnataka Bangalore India

A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy Deep learning Guava disease Leaf images Machine learning

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Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K-Nearest Neighbor

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Journal of Artificial Intelligence and technology 2023年第3期3卷 114-118页

作者： Edwin R.Arboleda Department of Computer and Electronics Engineering College of Engineering and Information TechnologyCavite State UniversityIndangCavitePhilippines

In this study,texture features namely entropy,contrast,energy,and homogeneity are extracted from mature and immature coffee beans using image processing,and the values are inputted into MATLAB’s Classification Learner App for *** the 23 machine-learning algorithms,the best performance was achieved by medium K-nearest neighbor which has 97%accuracy and 0.14574 seconds in *** compared with previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans,it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.

关键词： coffee energy entropy homogeneity machine learning texture

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A Hyperbolic Micromagnet for Multiple Spin Qubits with Fast Rabi Oscillations and High Addressability

IEEE Electron Device Letters

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IEEE Electron Device Letters 2024年第4期45卷 526-529页

作者： Chang, Che-Hao Li, Yu-Cheng Wu, Yu-Jui Liao, Chen-Yao Lin, Min-Jui Tsao, Hung-Yu Li, Jiun-Yun Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Graduate School of Advanced Technology Taipei10617 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan

We demonstrate a hyperbolic micromagnet to increase the manipulation speed of electron spins in silicon quantum dots using electric dipole spin resonance (EDSR). For single qubits, the hyperbolic magnet provides a large gradient of transverse magnetic fields of 4.9 mT/nm with the corresponding Rabi frequency ( 69 MHz). We then demonstrate that this hyperbolic micromagnet can drive a linear array of qubits with Rabi frequencies of 19 69 MHz and a high addressability (minimum difference of Larmor frequencies) up to 590 MHz to suppress the crosstalk between different qubits. We show that 15 qubits can be effectively addressed for high fidelity by this hyperbolic micromagnet. © 1980-2012 IEEE.

关键词： Magnetic fields

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