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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Bhavani, Akkisetty Jyothsna Naga Sree, Gorikapudi Bhanu Yasaswini, Addanki Brahmam, Borigorla Padmanaban, K. Kumar, Anguraj Dinesh Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350346640

In recent days, Image classification and detection technique has become an important and more essential in the Image processing research field. Creating effective face detection is an essential aspect of handling the detection mechanism, Tracking mechanism and Validation mechanism. The classical methods used for face detection do not have sufficient output. This research paper presents various studies and how machine learning methods are become to solve many challenges present in the face detection system. The first phase of work has a classification model with support vector machines, decision trees and Hybrid Ensemble Transfer learning algorithm. The second phase of work is investigated with real-the world's most popular dataset from World Masked Face Image Dataset and Label Faces in the wild (RMFD). Moreover, the experiment, results show how better accuracy and fast computation which has been achieved by Hybrid Ensemble algorithm with SVM and Decision Trees machine learning techniques. This research helps to assist many social applications such as during pandemics like covid-19 and personal identity, it can be verifying the mask-worn persons. © 2023 IEEE.

关键词： Decision trees

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Exploring the Applications, Challenges, and Issues of Sentiment Analysis 7

Exploring the Applications, Challenges, and Issues of Sentim...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Pulikonda, Venkata Ayyappa Vanukuru, Dharmic Navali, Mohammed Sehan Motepalli, Bhuvaneswari Swathi, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9781665464086

Everyone in this rapid evolution generation makes use of online resources. There is a huge amount of textual content available online from several sources, including social media apps, e-commerce sites, movie recommendation system, etc. Everyone in the modern society provides a variety of reviews for a specific product or item. An effective and smart technique is required that can analyze and provide the polarity of this textual data. since it is impossible to manually review such a vast volume of user-generated data. For this user-generated data, automatic sentiment classification is possible using a variety of tools and methods. Opinion mining and sentiment classification benefit from the dependable and accuracy of machine learning. Moreover, the sentiment analysis and evaluation process are fraught with difficulties. These complexities make it difficult to interpret sentiments accurately and figure out the right sentiment polarity. This article describes an overview on sentiment analysis(S A) applications, challenges, and issues encountere d by researchers, with the goal of providing a systematic review on sentiment analysis and related areas for future research. © 2023 IEEE.

关键词： Sentiment analysis

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Remote Sensing-Based Ecosystem Monitoring and Disaster Management in Urban Environments Using Machine Learnings

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 319-327页

作者： Mohan, M. Macharla, Anila Parthasarathi, P. Sharan, Bediga Nageswaran, A. R. M, Balajee Department of Computer Science and Engineering SRM University Delhi-NCR Haryana Sonepat India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathy India School of Computer Science & amp Artificial Intelligence SR University Telangana Warangal India Department of CSE Tagore Engineering College Rathinamangalam Vandalur Chennai 600 127 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

A key component of managing natural resources is the use and cover of the land. Maps of environmental changes are created using it in order to monitor ecosystems. For forestry, urban planning and agriculture, automatic mapping has many benefits. The science of remote sensing has benefited greatly from the development of deep learning techniques, which have yielded impressive results in image classification. This research proposes novel technique in ecosystem monitoring with disaster management in urban environments based on hyperspectral image analysis using a machine learning model. Here the input has been collected as hyperspectral image as well as processed for noise removal, normalisation and smoothening. Ecosystem monitoring is carried out utilizing Gaussian attention linear discriminant logistic regression. Then, the disaster management has been carried out based on the ecosystem monitoring model using cloud-based genetic spatio algorithm. Experimental analysis is carried out in terms of prediction accuracy, precision, F-measure, recall and RMSE for various hyperspectral images: prediction accuracy of 96%, precision of 97%, F-measure of 88%, recall of 95% and RMSE of 60% for proposed technique. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Disaster management Ecosystem monitoring Hyperspectral image analysis Machine learning model Urban environments

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Comparative Analysis of Generative AI Techniques for Addressing the Tabular Data Generation Problem in Medical Records

Comparative Analysis of Generative AI Techniques for Address...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Aravinth, S.S. Srithar, S. Joseph, K. Pranay Gopala Anil Varma, U. Kiran, G. Madhu Jonna, Venkatesh Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350306927

This research paper explores the use of Generative Artificial Intelligence (GAI) techniques to create synthetic medical datasets that balance data realism and privacy preservation. The sensitivity of medical records poses challenges for data access and sharing, making GAI a promising solution. The study evaluates five key approaches - StyleGAN2, CLIP, T5, ViT, and specialized Tabular GANs - across three critical dimensions: distribution fidelity, attribute correlation preservation, and pattern recognition accuracy. The results reveal the strengths and limitations of each technique in generating realistic medical data. StyleGAN2, CLIP, and T5 excel in all dimensions, making them ideal for various applications requiring high-quality synthetic medical datasets. ViT shows promise but may need fine-tuning for specific use cases, while specialized Tabular GANs demonstrate potential but vary in performance. This comparative analysis provides valuable insights for researchers and practitioners at the intersection of Generative AI and healthcare data generation. It underscores the potential of Generative AI in addressing the tabular data generation challenge in medical records, offering realistic and privacy-conscious alternatives for data utilization and model training. © 2023 IEEE.

关键词： Data privacy

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Hybrid Grey Wolf Algorithmic Approach for Optimizing Workflow Scheduling with Budget and Deadline Constraints in IaaS Clouds 15

Hybrid Grey Wolf Algorithmic Approach for Optimizing Workflo...

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

作者： Kolla, Sai Sampath Ramesh, Dharavath Santosh, Thakur Narvaneni, Ramarao Department of Computer Science and Engineering Dhanbad India Mahindra University Centre for Life Sciences Ecole Centrale School of Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350370249

Efficiently allocating virtual machines (VMs) to tasks within cloud computing environments is crucial for optimizing resource usage and cutting operational expenses. Traditional methods often struggle to balance cost efficiency and timeliness, especially in intricate cloud workflow setups. This paper introduces a novel hybridization approach merging Simulated Annealing (SA) and Grey Wolf Optimizer (GWO) to tackle these challenges. HGWO utilizes SA's exploratory prowess to escape local optima and leverages GWO's exploitative abilities to refine solutions towards global optimality. Additionally, the algorithm includes a penalty function to rigorously eliminate solutions that violate predefined budget and deadline constraints, ensuring adherence to Quality of Service (QoS) requirements. Performance evaluation via extensive simulations, modeling realistic cloud workflow scenarios, demonstrates that the hybrid algorithm consistently surpasses existing cost reduction and deadline adherence methods, making it a potent tool for cloud service providers seeking to boost resource allocation efficiency. This study's implications indicate that integrating diverse optimization techniques can yield more robust solutions in cloud workflow management, fostering adaptive and scalable cloud computing architectures. © 2024 IEEE.

关键词： Cloud computing Hybrid algorithms Makespan Optimization Penalty function Workflow scheduling

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A Comparative Survey on K-Means and Hierarchical Clustering in E-Commerce Systems

A Comparative Survey on K-Means and Hierarchical Clustering ...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Reddy, Chinnam Sasidhar Rao, N. Sai Kiran Deepak Sisir, Atkuri Raju, Vysyaraju Shanmukha Srinivasa Aravinth, S.S. Koneru Lakshmaiah Education Foundation Computer Science and Engineering Department Guntur India

ISBN: (纸本)9781665474511

E-commerce systems have grown in popularity and are now used in almost every business. A platform for online product marketing and customer promotion is an e-commerce system. Customer clustering is defined as the process of categorizing consumers into sections that share resembling characteristics. To maximize each customer's profit to the business, customer clustering's goal is to help decide how to engage clients in each category. To facilitate customer needs by improvising products and optimizing services, businesses can identify their most profitable customers by segmenting their customer base. As a result, customer clustering assists E-commerce systems in promoting the appropriate product to the appropriate customer to increase profits. Customer clustering factors include geographic, psychological, behavioral, and demographic factors. The consumer's behavioral factor has been highlighted in this research. As a result, to discover the consumption behavior of the E-shopping system, customers will be analyzed using several clustering algorithms. Clustering seeks to maximize experimental similarity within a cluster while minimizing dissimilarity between clusters. Customers' age, gender, income, expenditure rate, etc. are correlated in this study. To assist vendors in identifying and concentrating on the most profitable segments of the market as opposed to the least profitable segments, this study compared several clustering techniques to find which technique is more accurate to cluster customer behavior. A significant role for this kind of analysis in business improvement to keep customers for a long time and boost business profits, businesses group their customers based on similar behavioral traits. It also enables the maximum disclosure of online offers to attract the attention of potential customers. A learning algorithm called K-Means and an unsupervised algorithm hierarchical clustering is applied to a customer dataset to compare which strategy gives most accu

关键词： K-means clustering

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN computer science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Sharma, Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering design limitations. On the other hand, metaheuristic approaches promise to find global optima in real-world optimization problems, even in nonlinear and multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, whereas SBO finds the best cluster heads by considering communication range. By comparing the suggested approach to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster performance, simplifies cluster head selection in WSNs, and increases network efficiency overall, according to the results. To prove its efficacy in enhancing network performance, the suggested solution outperforms standalone SBO and CSA approaches across a range of performance parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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A Performance Analysis of Machine Learning Algorithms for Malaria Parasite Detection using Microscopic Images 5

A Performance Analysis of Machine Learning Algorithms for Ma...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Kundu, Tamal Kumar Anguraj, Dinesh Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9781665474672

Plasmodium parasites are the cause of malaria, a blood-borne disease spread by mosquitoes. Preparing a blood sample, staining it, then using a microscope to look at the stained blood smear to determine which parasite genus is Plasmodium. is the standard method for detecting malaria, a promising area of research is computer-aided Plasmodium detection. For the purpose of detecting Plasmodium on blood smear images obtained through conventional microscopy, this study examines the efficacy of various machine learning strategies in this paper. To target effected blood smears for detection of malaria, this research work employs different algorithms of machine learning in contrast to the conventional approach and has some issues with sensitivity and specificity. Most malaria diagnoses are made using standard microscopy. Microscopy has also shown a range of clinical and transmission situations to have varied sensitivity and specificity. The suggested method does not require experts or blood stains;instead, it uses images of patient to detect malaria. © 2023 IEEE.

关键词： Diseases

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Using Image Classification-based RBC Extraction for Malarial Parasite Identification in Peripheral Blood Smear 2

Using Image Classification-based RBC Extraction for Malarial...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Kundu, Tamal Kumar Anguraj, Dinesh Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vijayawada India

ISBN: (纸本)9781665491990

Malaria is a dangerous, irresistible, and perilous mosquito-borne blood disease brought about by Plasmodium parasites. The traditional and most standard approach to diagnosing malaria is by outwardly analyzing blood smears through conventional microscope for parasite-contaminated red platelets under the conventional microscope by qualified specialists. This technique consumes more time and the conclusion relies upon the experience and the information provided by the individual performing the assessment. A red platelet is viewed as contaminated if something like one parasite can be identified inside its inside. White platelets and free-drifting parasites are not thought of. The present status of the craftsmanship includes manual counting by a research facility expert or other person, who can recognize staining antiquities from actual nuclei, white platelets, and (contingent upon explicit prerequisites) life cycle and types of malarial parasites. In any case, the commonsense presentation has not been sufficient up until this point. This gives us all the inspiration to make malaria location and finding quick, simple, and proficient. The main aim of this study is to assemble a model to recognize cells from images of various cells in slender blood smear on standard magnifying lens slides and characterize them as either contaminated or uninfected with right on time and compelling testing utilizing image processing. This image processing method focusing on automated screening of malarial parasite contamination in microscopic image of thin blood stain. © 2023 IEEE.

关键词： Platelets

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Unveiling Hidden Ties: Exploring Community Structures in Social Networks using Advanced Graph Analytics

Unveiling Hidden Ties: Exploring Community Structures in Soc...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Aravinth, S.S. Kavitha, M. Namitha, J.S.N. Shri Harshini, P. Naveena Rani, K. Sri Thanmayee, S. Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350306927

In an era characterized by ubiquitous online social interactions, understanding the intricate structures and hidden connections within social networks is paramount. This research embarks on a journey to uncover concealed ties and explore the captivating world of community structures in social networks, leveraging the power of advanced graph analytics. The primary purpose is to reveal the latent bonds and relationships that often lie beneath the surface of seemingly unstructured social networks. By doing so, this study seeks to shed light on how users organize themselves into communities based on shared interests, affiliations, or patterns of interaction. This exploration aims to deepen comprehension of the underlying dynamics that shape digital societies. To achieve this, advanced graph analytics techniques, including modularity optimization and community detection algorithms, are employed. Social network data is analyzed to identify clusters of users with strong intra-cluster connections and limited inter-cluster links. Bridging users, pivotal in connecting disparate communities, are also unveiled through this methodology. These techniques allow for the revelation of community structures and the quantification of their quality through modularity scores. This research uncovers the presence of distinct and meaningful communities within diverse social networks. These communities are marked by shared interests and distinct network behaviors. Furthermore, the study identifies and analyzes bridging users, pivotal figures who facilitate interactions between otherwise disconnected communities. The modularity scores obtained underscore the effectiveness of this approach in emphasizing community cohesion. © 2023 IEEE.

关键词： Population dynamics

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