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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Awasthi, Naimisha Gautam, Prateek Raj Sharma, Anuj Kumar Aktu Centre for Advanced Studies Department of Computer Science and Engineering Lucknow India Upes School of Computer Science Dehradun India Aktu Centre For Advanced Studies Department of Mechatronics Lucknow India

ISBN: (纸本)9798350370249

The internet is extremely valuable, but it is also vulnerable to assault. Malware is a persistent and dynamic danger. Malware assaults entail introducing malicious code into software. The task was handled using a variety of machine learning approaches. Previous research utilized various methods, such as chi-square, Fisher's score, and Forward/Backward methods. A recent work used a wrapper strategy of Recursive Feature Elimination with Cross-Validation using the nonlinear Support Vector Machine (SVM) as an estimator. This lowered the number of features from 215 to 74, yielding an accuracy percentage of 95.58%. Our solution improves on the previous strategy by using the same dataset and a decision tree with Recursive Feature Elimination with Cross-Validation using Decision Tree to identify 53 out of 215 feature. Classifiers other than Naive Bayes and Logistic Regression are all obtaining the highest accuracy of 98%. This paper decreases the number of features by using Gini Impurity and Cross Validation for detection of Android Malware. © 2024 IEEE.

关键词： Decision Tree Feature Importance Feature Selection Machine Learning Malware Detection Recursive Feature Elimination

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Classification of Soil Moisture Content with the Application of Deep Learning

Classification of Soil Moisture Content with the Application...

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

作者： Ansari, Mohammad Shaharyar Gautam, Prateek Raj Centre for Advanced Studies AKTU Department of Computer Science and Engineering Lucknow India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798350364088

India is an agricultural-oriented nation where soil moisture plays a vital role in the growth of plants and crops. Measuring soil moisture and maintaining a balance in the soil moisture content help sustain water resources. The traditional method for calculating the soil moisture content is to calculate the weight of moisture soil and dry soil differences. With time passage, different techniques are introduced based on different sensors. Recently, machine learning and deep learning have emerged as powerful tools and can be used for the classification of different levels of soil moisture. Convolution Neural Network is a technique used in the field of deep learning for the classification of images. This algorithm is used to analyze the images on various factors like texture, color, hue, and shapes. In this research, images have been collected from the internet image search and stored in separate folders according to their class. CNN is applied to the created dataset and after tuning the hyperparameters model reaches an accuracy of 73%. © 2024 IEEE.

关键词： Soil moisture

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Segmentation of Skin Lesion Image Using FCM with Color Transformation Saliency 1

Segmentation of Skin Lesion Image Using FCM with Color Trans...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Bavya, S. Saravana Kumar, V. Kavitha, M. Anantha SivaPrakasam, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science & Engineering Chennai India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India S A Engineering College Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science & Technology Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

In the majority of Western nations including America, Australia and Europe, skin cancer is badly-behaved. Skin concealing, inadequacy of Sun-lights, climate, age, and inherited are major risk factors. Early identification of skin cancer and proceed the treatment can avoid death. The abnormal skin region determines where the skin diseases had been found. Various sorts of skin ailments be existent currently. Melanoma is the sort of skin threat which has grows the greater part of mortality rate. The timely identification and intercession of melanoma include myriad changes of fix. Melanoma has been recognized by shape, size, concealing and outward of the skin injury. This paper demonstration about the traditional clustering technique in particular Fuzzy Clustering technique is acted in these skin lesion pictures. This strategy is segmenting this melanoma from these dermoscopic images effectively. The presentation of this strategy is assessed as far as pixel grouped and time complexity. © 2024 IEEE.

关键词： Feature extraction

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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A Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms 8

A Mango Disease Prediction for Smart Agiculture using Machin...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lavanya, K. Packialatha, A. Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Chennai Tamilnadu Pallavaram India

ISBN: (纸本)9798350376425

The country's economy relies heavily on agriculture, and the health of the crops is essential to its success. Crop diseases that go undetected can cost the agriculture industry. Thus early detection and identification are crucial. It is possible to avoid crop diseases from destroying the harvest if they are accurately diagnosed and detected. Because healthy and diseased plants appear identical in their early stages, farmers cannot distinguish between the two by watching the crop leaf. India exports vast mangoes, making it an economically and environmentally significant fruit. About 1500 mango species are grown in India, with over 1000 commercial types. There are a lot of diseases that harm mangoes, affecting their look, taste, and economy. The "Prediction of Disease in Mango Fruit Crops"A complex warning system that uses machine learning and IoT. One of the main objectives is to develop a system that can forecast disease outbreaks on mango fruit harvests using historical weather information and crop yield Mango trees in India are plagued by a fungus called Anthracnose, which is the most frequent disease of its kind. Anthracnose, a highly contagious fungus, requires a quick and accurate method of diagnosis. As a result, an in-depth examination of the plants is essential before initiating any control measures. This study examines machine learning(ML) and deep learning (DL) strategies for detecting and classifying mango plant diseases. The performance of ML and DL-based classification models for mango crops and their datasets and feature extraction approachesare examined in this work. Finally, a variety of issues involved in plant disease identification are explored. © 2024 IEEE.

关键词： Smart agriculture

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Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources 10

Harmonizing Healthcare Perspectives and In-Depth Strategy fo...

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10th International Conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Manikandan, A. Thirumal, S. Kumar, N. Vels Institute of Science Technology &advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration. © 2024 IEEE.

关键词： Electronic health record

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IoT Device for Food Storage 8

IoT Device for Food Storage

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Mandal, Lopa Vardhan, Guttapalem Reddy Vishnu Bharani, G.S. Ahmed, Jawad Kavin, K. Reddy, Nandi Sasidhar Alliance School of Advanced Computing Alliance University Department of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331520762

The integration of IoT technology provides realtime, data-driven monitoring that reduces waste and maintains the quality of the food, changing the way food is preserved and managed. This research focuses on the development and implementation of an Arduino-based IoT device for monitoring freshness, temperature, and humidity in storage food environments. Equipped with an MQ-4 methane sensor and DHT11 sensor, the system detects spoilage indicators and environmental conditions and opens an exhaust fan if the moisture has been in excess. The BLYNK IoT app provides instant information to the user about storage conditions, thus ensuring efficient and proactive food management. This product finds answers to some of the main food-related storage challenges by offering scalable and effective solutions for enhanced freshness and reduced spoilage. © 2025 IEEE.

关键词： Spoilage

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Enhancing air quality prediction using machine learning algorithms and boosting techniques 5th

Enhancing air quality prediction using machine learning algo...

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Sajja, Rushvi Ahmed, S.K. Feroz Bodapati, Jyostna Devi Yenduri, Lakshmana Kumar Department of Advanced Computer Science and Engineering VFSTR Deemed to be University Andhra Pradesh India Department of Advanced Computer Science and Engineering Vignan University Andhra Pradesh India IEEE San Francisco Bay Area United States

ISBN: (纸本)9781032642987

Air pollution, exacerbated by industrialization and urbanization, poses a significant environmental and public health challenge worldwide. Particular concern is directed towards PM2.5, which poses greater health risks than other airborne impurities combined. Accurately predicting PM2.5 levels is crucial for effective pollution management. This study focuses on predicting the Air Quality Index (AQI) as a measure of air pollution, employing machine learning algorithms to enhance predictive accuracy. Specifically, we utilize the Random Forest Classifier and AdaBoost boosting algorithm to develop a predictive model. Our approach involves data categorization and sensitivity analysis to ensure robust air quality predictions. © 2024 The Author(s).

关键词： Air quality

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CNN Models Transfer Learning Based Ship Classification for Intelligent Ship Routing

CNN Models Transfer Learning Based Ship Classification for I...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Tiwari, Brij Kishor Munagala, Rohini Sai Mannarugudi, Sai Dheeraj Vignan's Foundation for Science Technology and Research Department of Advanced Computer Science and Engineering Guntur522213 India

ISBN: (纸本)9798350376876

Collision free autonomous navigation is challenging area of interest for the shipping industry, and an accurately classifying the ships according to their size and type classes is a crucial undertaking in marine operations. Accurate prediction and identification will improve the maritime security and navigation safety. However, automatic ship classification is a difficult subject because of the wide variety of ship designs and the complexity of visual data. We developed a deep learning model using transfer learning from pre-trained models in order to classify five different kinds of ships. We performed the experiment using different pre-trained convolution neural network model of the deep learning. In the transfer learning we considered the training of the dataset for 100 epochs. In this study, we investigated four deep learning model ResNet 50, VGG 16, VGG 19, and Inception V3. We carried out the identical procedure for training of each model by removing the top layer of pre-trained model and adding three dense layers for the top layers. The neuron counts are 1024, 512, 256 respectively followed by dropout layer of 0.5. Results of our model shows the maximum obtained accuracies are 86.66 % and from these we can observed that the developed model can be used for ship identification and classification and to take the decisions for collision free and smooth navigation. © 2024 IEEE.

关键词： Transfer learning

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Ensemble of diverse pre-trained convolutional neural networks for enhanced diabetic retinopathy image classification 5th

Ensemble of diverse pre-trained convolutional neural network...

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Ahmed, S. K. Feroz Chowdary, Y. Yaswanth Bodapati, Jyostna Devi Yenduri, Lakshmana Kumar Department of Advanced Computer Science and Engineering VFSTR Deemed to be University Andhra Pradesh India Department of Advanced Computer Science and Engineering Vignan University Andhra Pradesh India IEEE San Francisco Bay Area United States

ISBN: (纸本)9781032642987

Diabetic retinopathy (DR) presents a significant threat to individuals with diabetes, affecting the retinal blood vessels and potentially leading to vision impairment or loss. Early detection plays a pivotal role in mitigating vision-related complications, and recent advancements in deep learning offer promising avenues for enhancing diagnostic accuracy. In this study, we introduce an ensemble model comprising six distinct pre-trained Convolutional Neural Network (CNN) architectures for early detection of diabetic retinopathy. The proposed model encompasses various stages, including data collection, pre-processing, augmentation, and modeling. Leveraging the benchmark APTOS19 dataset, our ensemble approach achieves an impressive accuracy of 85.18%, surpassing the performance of individual models involved. © 2024 The Author(s).

关键词： Convolutional neural networks

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