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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Annepu, Visalakshi Bagadi, Kalapraveen Chirra, Venkata Rami Reddy Prashanth, Yalamanchili Nikhil, Akula Chaitanya, Anguluri Krishna VIT-AP University School of Computer Science and Engineering Amaravati India VIT-AP University School of Electronics Engineering Amaravati India

ISBN: (纸本)9789380544519

In this research work, we address the formidable challenge of automating the recognition and classification of vegetables by applying cutting-edge deep learning techniques. The manual identification of vegetables is a time-intensive process with the possibility of errors. To mitigate these challenges, we propose a hybrid deep learning approach that combines the strengths of VGG-16, VGG-19, and Inception V3 models. By leveraging the complementary features of these architectures, our hybrid model achieves enhanced accuracy in vegetable classification. Integrating multiple architectures strengthens the model's ability to discern intricate patterns and features, thereby improving its adaptability to a diverse range of vegetables, irrespective of colour, texture, and size variations. Moreover, the hybrid deep learning model exhibits remarkable efficiency, facilitating rapid processing and reducing the time-consuming nature of manual identification. Our research presents a notable advancement in vegetable recognition, offering a reliable and efficient solution for automated vegetable classification systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Learning Techniques Inception V3 Vegetable Recognition VGG-16 VGG-19

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Effective Hyper-Parameter Tuning of Machine Learning Model for Analysis Drinking Water Quality 15

Effective Hyper-Parameter Tuning of Machine Learning Model f...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Tanwar, Neha Turukmane, Anil V. School of Computer Science & Engineering VIT-AP University Vijayawada AP Amaravati India

ISBN: (纸本)9798331300579

The water quality of the river Krishna was carried out. Thermal conductivity, dissolved oxygen, total particulates in solution, biochemical oxygen demand, and pH levels were among these parameters. For a thorough knowledge of the Krishna River's overall water quality, each of these measurements is essential. This comprehensive technique enables a detailed evaluation that considers several elements of the river's environmental conditions and composition. When evaluating the river's water quality, several factors are crucial. In the Indian state of Karnataka, scientists measured the water quality at six different locations along the Krishna River. The Krishna River, which originates in the Bay of Bengal and travels around 1400 kilometers, is the fourth biggest river in India. Only the 483 km or so of the Krishna River that flows through the state of Karnataka was taken into consideration for our analysis. Rapid industrialization has recently been applied to the mineral-rich river basin, causing pollution. Unusable for human use, river water is inevitably going to get contaminated by a variety of contaminants, including industrial, agricultural, and municipal waste. The current manual procedure, which is conventional, is a very sluggish operation. Staff must collect water samples from the location, transport them to the laboratory, and analyze the samples for a variety of water characteristics. This is an expensive and time-consuming procedure. Therefore, the people living in the river basin region are not able to get timely information regarding the quality of the water. This feature makes it perfect for real-time water quality analysis of samples collected from the Krishna River. Water quality has a profound influence on the health of ecosystems and the welfare of human beings. Both people and animals may get sick from water pollution. Rivers are now one of the most widely utilized natural water sources on the planet, but in the last ten years, human activity and cli

关键词： Biochemical oxygen demand

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Comparative Analysis of Multiple Methods Utilized for Road Pothole Identification Using Deep Learning 5th

Comparative Analysis of Multiple Methods Utilized for Road P...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Tanwar, Neha Turukmane, Anil V. School of Computer Science & Engineering VIT-AP University Amaravati AP Vijayawada India

ISBN: (纸本)9789819760350

The public’s safety that asphalt roads be repaired and maintained by deep learning techniques. It is important to determine the current road pavement condition before making any plans for preventative maintenance on a road or other infrastructure. Damaged road has cracks, potholes, lines, and shade. Within the scope of this research, we investigated several approaches to pothole detection and developed an effective modified MobileNetV2 (MMNV2) technique. In this study, a modified version of the MobileNetV2 model is proposed for use in feature extraction, picture classification, and detection using deep learning (DL) by using the transfer learning approach. A pretrained MNV2 model was given an additional five layers in order to make it work better overall and categorize normal things more accurately, and pothole images using this method, a Python model that was trained and tested using 2,000 images of road pavement was produced. When transfer learning was combined with a deep neural network (DNN) architecture, the outcomes of this investigation demonstrated that our MMNV2 method was successful. Timely detection and repair of defects can optimize resource allocation, leading to cost reductions in road maintenance and enhanced operational efficiency. Consequently, pothole identification is essential for protecting the safety of roads, enhancing the flow of traffic, preserving infrastructure, and making the most efficient use of available resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Resource allocation

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Fuzzy Logic-Based Crop Diversification to Increase Crop Yield 7th

Fuzzy Logic-Based Crop Diversification to Increase Crop Yiel...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Divya Meena, S. Meghana, Pokala Rama Sındhu, Chennareddy Saı Varshını, Kopparla Reddy, Chıttemreddy Gnanendar Sheela, J. School of Computer Science and Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789819950140

Nowadays, farmers are facing a major loss in crop production due to various reasons such as reduction in soil fertility, lack of irrigation facilities, lack of adequate knowledge on the suitable crop for the persisting soil. A variety of technological and infrastructural variables have an impact on crop diversity. Crop diversification has an impact on productivity, according to the findings. Economic considerations are having an ever-growing impact on Indian agriculture. This shouldn't come as a surprise because improving infrastructure, expanding irrigation, reaching remote markets, developing drought-resistant crops, and spreading these technologies have all reduced the influence of non-economic variables on even small farmers’ crop selection. In this paper we attempt to predict the suitable crops for the given area of land using various parameters such as nutrient content in soil, meteorological parameters and geographical conditions and of the land by using fuzzy logic-based crop diversification system to suggest rural farmers from which farmer can cultivate multiple crops in the same area which can profit them economically. The proposed model has been planned to deal with the ten main crops grown in Andhra Pradesh such as Chili, Cotton, Groundnut, Paddy, Sugarcane, Tobacco, Mango, Millets, Pulses, Wheat. Different fuzzy rule bases were created for every crop to achieve rapid parallel processing. In this proposed model we build the required rules to establish a relationship between the input and output parameters and we established those rules and got desired output using fuzzy logic with a good accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Agricultural Yield Forecasting with the Fusion of Transfer Learning and LSTM approach 2

Agricultural Yield Forecasting with the Fusion of Transfer L...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Swanth, Boppudi Sheela, J. School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9798350327533

This study addresses the challenges inherent in crop yield prediction by proposing a novel approach that combines transfer learning and Long Short-Term Memory (LSTM) techniques. Existing methodologies face obstacles related to limited accuracy and adaptability, particularly in handling diverse agricultural datasets. Recent techniques, including transfer learning, have shown promise in leveraging knowledge from disparate domains, but challenges persist in effectively applying these techniques to agricultural data. The proposed approach aims to overcome these challenges by synergistically employing transfer learning and LSTM. Transfer learning enhances the model's analytical capabilities by leveraging knowledge from various domains, providing a broader foundation for agricultural dataset analysis. Additionally, LSTM, renowned for capturing temporal dependencies, further refines the model's predictive capacity, addressing challenges related to dynamic agricultural processes and environmental factors. The research methodology involves the implementation and fine-Tuning of transfer learning and LSTM models on relevant agricultural datasets, followed by a rigorous evaluation of their performance. The study anticipates uncovering a novel and effective solution for optimizing crop yield predictions. By explicitly addressing challenges faced by current systems and integrating recent techniques, this research contributes to precision farming and sustainable agricultural practices. The anticipated findings hold the potential to empower farmers with valuable insights, facilitating improved decision-making and resource allocation in the realm of agriculture. © 2024 IEEE.

关键词： crop yield forecast Long Short-Term Memory Mean Squared Error Root Mean Square Error Transfer Learning

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Revolutionizing wildlife protection: a novel approach combining deep learning and night-time surveillance

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Multimedia Tools and applications 2024年 1-35页

作者： Madhasu, Nithya Pande, Sagar Dhanraj VIT-AP University Andhra Pradesh India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

The increasing instances of animals encroaching on human settlements, as well as the illicit trafficking of wildlife, have prompted immediate actions to protect the natural heritage. In addition to this, the difficulties of night-time animal surveillance are also being faced. This paper highlights the urgent necessity for comprehensive animal welfare monitoring and effective anti-illegal trafficking prevention. The expensive cost of installing night vision cameras heightens the necessity of locating an affordable and effective solution. To overcome these issues, a unique strategy, combining colorization utilizing Customised Conditional GAN (Generative Adversarial Net) for night-time applications and YOLO-CNAS (You Only Look Once-Customised Neural Architecture Search) classification for intelligent wildlife detection is proposed. Colorization during the night enables the discovery of critical nocturnal behaviors, encouraging a greater knowledge and relationship with nature. Using these powerful deep learning algorithms helps to recognize the wildlife throughout both daylight and night-time hours, as well as smugglers trespassing into the forest. The suggested method obtains an amazing testing accuracy rate that has improved from 55.73% to 72.54% and then finally to 94.67%, demonstrating the revolutionary approach's potential for animal protection. The extensive dataset used for training and assessment was meticulously sourced from various websites including iNaturalist, Unsplash, and Pexels. These platforms provided a rich array of diverse images. During the pressing need to protect the nation's irreplaceable heritage, the intelligent model provides a ray of hope. Conservationists and policymakers may work together to safeguard the natural heritage by enabling effective wildlife surveillance and maintaining a happy coexistence between humans and animals for future generations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of S

关键词： Animals

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Age and Mood Based Music Recommendation System Using Convolutional Neural Networks 10

Age and Mood Based Music Recommendation System Using Convolu...

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

作者： Guthula, Manvitha Sri Maria, Azees School of Computer Science and Engineering VIT-AP University Amaravti India

ISBN: (纸本)9798350384369

Music serves as an emotional compass, guiding listeners through a myriad of moods and moments with its melodic embrace. It has the profound ability to elevate spirits, soothe minds, and boost productivity, making the art of recommending the right track at the right time a pursuit of emotional synchronization. This paper explores the intersection of age, mood, and music preferences through the lens of Convolutional Neural Networks (CNNs), aiming to identify the most effective model for extracting relevant features from facial expressions and demographic data. By harnessing the power of deep learning CNN architectures, including AlexNet, GoogleLeNet, Lenet, ResNet, VGG-16, and VGG-19, music selections can be tailored to fit the unique emotional and demographic contours of each user. Our comparative analysis is grounded on performance metrics such as accuracy and loss, leveraging the UTK-Face dataset and Face expression recognition dataset to train and validate our models. © 2024 IEEE.

关键词： Convolution

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PII Data Detection using Keras & NLP 3

PII Data Detection using Keras & NLP

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Kommathoti, Yanni Rohan Neha, Palanati Pabbathi, Samudra Naseeba, Beebi School of Computer Science and Engineering VIT-AP University Vijayawada India

ISBN: (纸本)9798331518592

The research presents a reliable and effective method for automatically identifying and categorizing PII items in various textual datasets using advanced deep learning models, specifically KerasNLP, DeBERTaV3, and DistilBERT. Sensitive information including names, addresses, and social security numbers are included in personally identifiable information. The research makes use of neural network technology to automatically recognize and categorize PII elements found in textual data. A comprehensive method that includes training strategies specifically designed for PII detection, model architecture design, and preprocessing stages. The proposed model has demonstrated its ability to accurately identify Personally Identifiable Information (PII) by achieving an accuracy of 89.8% and a minimal loss of 0.0004% when adjusting to longer input sequences. © 2024 IEEE.

关键词： Deep learning

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Beyond the Black Box: Interpretable Machine Learning for Airplane Crash Severity Classification 5

Beyond the Black Box: Interpretable Machine Learning for Air...

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5th International Conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Siddique Ibrahim, S.P. Sai, Kotha Dinesh Komal, Garlapati K V S Sai Yasaswini, N.S.N.S.M. Sree, Guduri Divya Sai School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9798331530013

Airplane crash severity classification is crucial for enhancing aviation safety and risk assessment. This study proposes a method to classify airplane crashes as Severe or Non-Severe based on the number of fatalities, using a dataset containing various crash details, including Date, Location, Operator, Fatalities, and more. A new feature, Crash_Severity, is introduced to label crashes with more than 15 fatalities as Severe, and others as Non-Severe. Four Classification models were employed to classify crash severity. The models achieved accuracies of 97.4%, 93.8%, 88.75%, and 58.23%, respectively. The results indicate that Logistic Regression and Random Forest performed very well, possibly due to the clear distinction between the two classes based on fatalities. In contrast, SVC struggled, possibly due to the linear separability limitations in the dataset. These findings demonstrate that fatalities serve as a strong predictor for crash severity, with Random Forest and Logistic Regression being the most effective models for this task. Future work may explore incorporating additional features to further enhance model performance © 2024 IEEE.

关键词： Aircraft accidents

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AURA: Adaptive Unified Real-Time Analytics for IoT Intrusion Detection 18

AURA: Adaptive Unified Real-Time Analytics for IoT Intrusion...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Mishra, Sanket Aravindan, V. Singh, Rajkanwar Singh, Vikash Kumar VIT-AP University School of Computer Science & Engineering Amaravati India

ISBN: (纸本)9798350391725

In high-volume, high-velocity contexts, threat identification requires effective real-time data stream analysis. This study offers a novel architecture - real-time processing of high-speed data streams - that is critical for effective threat identification in dynamic contexts. By using a publish-subscribe approach with apache Kafka, the system is able to manage differences in data volume between many nodes. Experiments on the CICIoV24 and the CICEVSE2024 datasets indicate that the XGBoost model performs better, with high accuracy and robustness against adversarial attacks. Its performance degrades during the HopSkipJump attack, however, defence training can help with it. Moreover, our analysis shows that RandomForest and ExtraTrees perform better in noisy data from the CICIoV24 and XGBoost perform better in noisy data from the CICEVSE24 dataset, emphasizing the importance of selecting algorithms based on performance indicators. The architecture utilizes PyFlink's distributed computation framework to improve computational efficiency for real-time processing and solves idea drift to ensure flexibility in changing data attributes. © 2024 IEEE.

关键词： Data streams

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