Revolutionizing Big Data with AI-Driven Hybrid Soft Computing Techniques

Revolutionizing Big Data with AI-Driven Hybrid
Soft Computing Techniques
Praveen Kumar Myakala ⋆, Anil Kumar Jonnalagadda , and Prudhvi Naayini
Independent Researchers
Dallas, Texas, USA
Abstract. The ever-growing complexity and scale of Big Data have rendered traditional computational
approaches insufficient, driving the need for innovative AI-driven solutions. This paper presents an ad-
vanced framework that integrates artificial intelligence (AI) and machine learning (ML) with hybrid soft
computing techniques, including fuzzy logic, deep neural networks, evolutionary algorithms, and swarm
intelligence. These methods collectively address challenges such as high dimensionality, uncertainty, real-
time processing, and scalability, thereby achieving enhanced accuracy, interpretability, and adaptability.
Cutting-edge strategies, such as adaptive neuro-fuzzy systems and deep neuro-evolution, enable transfor-
mative improvements across diverse domains, including healthcare, IoT, and social media. Experimental
evaluations on real-world datasets demonstrate significant advancements, including up to 20% faster pro-
cessing speeds and a 15% improvement in predictive accuracy compared to traditional method s. This
research underscores the pivotal role of AI-augmented soft computing in shaping the future of Big Data
analytics, offering robust and scalable solutions to meet evolving industrial demands. Furthermore, it
lays the foundation for developing next-generation systems capable of addressing emerging challenges in
data-driven decision-making across industries.
Keywords: Big Data, Soft Computing, Artificial Intelligence, Machine Learning, Hybrid Systems, Neuro-
Fuzzy, Deep Neuro-Evolution, Predictive Analytics
1 Introduction
This paper builds upon prior research [1], which addressed the challenges of Big Data
analytics using soft computing techniques. The current study extends this work by incor-
porating advanced hybrid methodologies, including adaptive neuro-fuzzy systems and deep
neuro-evolution, to address the evolving needs of scalability, uncertainty management, and
real-time processing in Big Data environments.
The exponential growth in the complexity, diversity, and volume of data—commonly
referred to as Big Data—has fundamentally transformed how organizations across indus-
tries operate. Big Data encompasses the five ”V”s: Volume, Velocity, Variety, Veracity,
and Value, each presenting unique challenges in data management, processing, and anal-
ysis [2], [3]. Traditional computational approaches, designed for smaller and less dynamic
datasets, often fail to keep pace with the demands of modern data environments. This gap
necessitates innovative solutions that leverage artificial intelligence (AI), machine learning
(ML), and advanced computational frameworks to unlock the transformative potential of
Big Data [4].
1.1 The Need for Hybrid Approaches
Big Data is often characterized by high-dimensionality, uncertainty, and noisy, incom-
plete information, all of which hinder effective analysis using traditional methods [5], [6].
Furthermore, real-time processing and scalability requirements amplify the challenges,
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Machine Learning and Applications: An International Journal (MLAIJ) Vol.12, No.1, March 2025
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DOI:10.5121/mlaij.2025.12101

especially as data streams continue to grow in both volume and velocity [7], [8]. Soft
computing techniques, such as fuzzy logic, neural networks, evolutionary algorithms, and
swarm intelligence, have emerged as promising solutions due to their inherent adaptabil-
ity and robustness in handling imprecise and uncertain data [9]. However, when used in
isolation, these techniques are often insufficient for addressing the multifaceted challenges
of Big Data. Hybrid approaches that synergize the strengths of these methods provide a
comprehensive and scalable solution to tackle these challenges [10], [11].
1.2 Overview of Contributions and Novelty
Introducing an advanced hybrid framework that integrates AI and ML with state-of-the-
art soft computing techniques [1]. The novelty of this framework lies in the incorporation of
cutting-edge strategies, such as adaptive neuro-fuzzy systems and deep neuro-evolution,
which address specific Big Data challenges in unique ways. Adaptive neuro-fuzzy sys-
tems combine the interpretability of fuzzy logic with the learning capabilities of neural
networks, making them highly effective for handling noisy and uncertain data [6]. Deep
neuro-evolution, on the other hand, leverages evolutionary algorithms to optimize deep
neural network architectures, enabling efficient processing of high-dimensional datasets
and improving scalability [10], [11].
The proposed framework extends beyond conventional methods by focusing on:
– Efficient feature extraction and selection to mitigate the curse of dimensionality [5].
– Robust handling of uncertainty and noise through fuzzy logic and evolutionary algo-
rithms [6].
– Real-time processing optimization using swarm intelligence and distributed computing
architectures [12].
1.3 Impact and Applications
This research demonstrates the versatility of the proposed framework through its applica-
tion in diverse domains, including healthcare, IoT, and social media analytics. For instance,
in healthcare, the framework improves predictive analytics and diagnostic accuracy in the
presence of incomplete or noisy patient records [13]. In IoT systems, it enables efficient
real-time data processing and anomaly detection, while in social media, it enhances senti-
ment analysis and trend prediction [14]. Experimental evaluations on real-world datasets
validate the framework’s effectiveness, showcasing up to 20% faster processing speeds and
a 15% improvement in predictive accuracy compared to traditional methods.
By addressing the limitations of existing approaches and introducing novel methodolo-
gies, this work paves the way for the development of next-generation Big Data analytics
systems. These systems hold the potential to revolutionize industries by enabling more
accurate, interpretable, and scalable data-driven decision-making.
2 Challenges in Big Data Analytics
The rapid expansion of data across industries has introduced several challenges that hinder
effective processing, management, and analysis. These challenges, often referred to as the
”five Vs” of Big Data—Volume, Velocity, Variety, Veracity, and Value—require advanced
methodologies to overcome. Below, we explore the most critical issues associated with Big
Data analytics, focusing on those that are particularly relevant to the proposed hybrid
framework.
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Fig. 1. Interconnected Challenges in Big Data Analytics
Figure 2 illustrates the interconnectedness of these challenges, emphasizing how they
collectively hinder effective Big Data analytics. Addressing these challenges in an inte-
grated manner is essential for achieving robust and scalable solutions.
2.1 Scalability
The ever-increasing volume of data generated by sources such as social media, IoT devices,
and scientific experiments necessitates scalable computational solutions. Traditional data
processing systems struggle to manage the massive size and speed of modern datasets,
leading to bottlenecks in performance and efficiency [7], [8]. While distributed computing
frameworks like Hadoop and Apache Spark have been widely adopted, further optimiza-
tion is required for real-time data streams and large-scale analytics [3]. As scalability is
fundamental to the success of any Big Data solution, it remains a primary focus of this
research.
2.2 High-Dimensionality
Modern datasets often contain thousands or even millions of features, resulting in high
dimensionality. This poses several challenges:
– Curse of Dimensionality: As dimensionality increases, the data space grows exponen-
tially, making it harder to identify meaningful patterns [5].
– Computational Complexity: Many algorithms degrade in performance as dimensionality
rises, leading to slower processing times and reduced accuracy.
– Overfitting: Models trained on high-dimensional data may capture noise rather than
meaningful patterns, impacting their generalization ability [10].
Feature selection and extraction techniques are critical for addressing these issues, and
they play a pivotal role in the proposed framework.
2.3 Uncertainty and Noise
Real-world datasets are often incomplete, noisy, or imprecise due to factors like measure-
ment errors, inconsistencies, and data entry inaccuracies [6]. For instance, sensor data in
IoT systems may contain missing or erroneous values due to hardware limitations [4]. These
issues can lead to unreliable results if not addressed effectively. This research prioritizes
managing uncertainty through:
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– Imputation techniques, such as k-nearest neighbors (kNN) and mean substitution.
– Noise filtering algorithms for cleaning datasets.
– Fuzzy logic systems, which excel in handling uncertainty and imprecision [9].
2.4 Real-Time Processing
Applications such as financial trading, social media analytics, and industrial monitoring
demand real-time data processing capabilities. Challenges in this area include:
– Low Latency: Minimizing delays to enable immediate decision-making.
– High Throughput: Managing continuous streams of data at scale.
– Adaptability: Ensuring models can handle rapidly evolving data distributions [8], [7].
The proposed framework integrates swarm intelligence and distributed architectures to
enhance real-time processing, making it one of the core challenges addressed.
2.5 Data Integration
Big Data is derived from heterogeneous sources, such as databases, sensors, and social
media, each with varying formats, standards, and quality. This creates several challenges:
– Data Inconsistency: Variations in formats, units, and schemas can lead to integration
errors.
– Quality Issues: Differences in accuracy, completeness, and reliability between sources
complicate analysis.
– Privacy Concerns: Integrating sensitive data requires adherence to regulations like
GDPR and HIPAA [3], [4].
Ontology-based methods and semantic web technologies are critical for improving data
integration. This research emphasizes addressing these challenges to ensure consistent and
reliable results.
2.6 Data Security and Privacy
The proliferation of Big Data introduces significant security and privacy concerns, par-
ticularly for sensitive information in healthcare, finance, and social media domains. Key
issues include:
– Data Breaches: Unauthorized access to sensitive data can have severe consequences.
– Data Misuse: Malicious exploitation of personal data, such as identity theft or discrim-
inatory profiling.
– Regulatory Compliance: Adhering to privacy regulations, such as GDPR and HIPAA,
is critical to maintaining user trust [15].
The use of advanced techniques, such as encryption, differential privacy, and federated
learning, is prioritized in this research to safeguard sensitive data [16].
3 Proposed Hybrid Framework
To address the challenges associated with Big Data analytics, we propose a hybrid frame-
work that integrates artificial intelligence (AI) and machine learning (ML) with advanced
soft computing techniques. This framework is designed to enhance scalability, manage
high-dimensional data, handle uncertainty, and optimize real-time processing, ensuring
robust and adaptable solutions across diverse domains [6], [10].
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3.1 Framework Architecture
The architecture of the proposed framework consists of five primary modules, each tailored
to address specific challenges in Big Data analytics:
– Data Preprocessing: This module handles missing values, noise, and data inconsisten-
cies to ensure clean and reliable input data [4]. Techniques include:
• Missing value imputation using k-nearest neighbors (kNN) and mean substitution
[5].
• Noise filtering with fuzzy logic [6].
• Data normalization for uniformity across heterogeneous sources.
– Feature Extraction and Selection: High-dimensional datasets are managed through:
• Dimensionality reduction using Principal Component Analysis (PCA) and autoen-
coders [10].
• Feature selection via evolutionary algorithms to identify the most relevant at-
tributes [11].
– Soft Computing Core: This module integrates advanced techniques to tackle uncer-
tainty and improve accuracy:
• Adaptive neuro-fuzzy systems for handling noise and imprecision [6].
• Deep neural networks for extracting meaningful patterns from complex datasets
[10].
• Swarm intelligence for optimizing clustering and resource allocation [12].
– Optimization Module: This module ensures efficient system performance by:
• Utilizing genetic algorithms for hyperparameter tuning and cluster optimization
[17].
• Employing particle swarm optimization for resource allocation in distributed sys-
tems [12].
– Result Aggregation and Interpretation: Outputs from different components are aggre-
gated and visualized through dashboards, enabling actionable insights and decision-
making.
Figure ?? illustrates the interactions between the five modules, emphasizing their role
in addressing the interconnected challenges of Big Data analytics.
3.2 Implementation Details
The proposed framework is implemented using the following tools and technologies:
– Programming Languages: Python is used for overall implementation, leveraging its
extensive ecosystem for machine learning and data processing [1].
– Libraries and Frameworks: TensorFlow and PyTorch for building neural networks,
Scikit-learn for feature selection, and Fuzzy Logic libraries for uncertainty management
[10], [6].
– Platforms: Distributed computing frameworks such as Apache Spark and Hadoop are
used for scalability and real-time processing [7].
– Visualization Tools: Dashboards are developed using Tableau and Matplotlib for result
aggregation and presentation.
5

Fig. 2. Proposed Framework Architecture
3.3 Key Innovations
The framework incorporates several novel features to address the limitations of existing
approaches:
– Adaptive Neuro-Fuzzy Systems: These combine the interpretability of fuzzy logic with
the learning capabilities of neural networks, providing robust solutions for noisy and
uncertain data [6].
– Deep Neuro-Evolution: This method optimizes the structure and parameters of deep
neural networks using evolutionary algorithms, enhancing scalability and efficiency
[11], [17].
– Real-Time Adaptation: The integration of swarm intelligence ensures that the frame-
work can dynamically adapt to changes in real-time data streams [12].
3.4 Domain Applications
The proposed framework is highly versatile and demonstrates effectiveness across various
domains:
– Healthcare: Enhances predictive analytics and diagnostic accuracy using incomplete or
noisy patient records [13].
– IoT Systems: Facilitates real-time data processing and anomaly detection in sensor
networks [4].
– Social Media: Improves sentiment analysis, trend prediction, and fake news detection
by efficiently handling high-dimensional text and image data [14].
3.5 Advantages of the Framework
The hybrid framework offers several advantages over traditional and state-of-the-art meth-
ods:
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– Scalability for processing large datasets in distributed environments [7].
– Robustness in managing noisy, incomplete, or uncertain data [6].
– Adaptability to evolving data streams and heterogeneous data sources [12].
– Enhanced accuracy and interpretability in predictive and analytical tasks [10].
By addressing critical challenges and incorporating innovative techniques, the proposed
framework sets a new benchmark for Big Data analytics, enabling actionable insights and
driving meaningful advancements across industries [1].
4 Experimental Setup
To evaluate the effectiveness of the proposed hybrid framework, a series of experiments
were conducted using real-world and synthetic datasets. The experimental setup is detailed
below, including the datasets, preprocessing methods, evaluation metrics, computational
environment, and comparative methods.
4.1 Datasets
The experiments utilized a combination of publicly available datasets and synthetic data
generated to mimic real-world scenarios:
– Healthcare Dataset: The MIMIC-III Clinical Database [18], containing de-identified
health-related data from critical care patients, was used to evaluate predictive analytics
and anomaly detection.
– IoT Sensor Data: The Intel Lab Sensor dataset [19], comprising sensor readings such
as temperature, humidity, and light, was employed for anomaly detection and real-time
data analysis.
– Social Media Dataset: The Sentiment140 dataset [20], containing labeled Twitter data,
was used for sentiment analysis and text classification tasks.
– Synthetic Data: High-dimensional synthetic datasets were generated using Scikit-learn’s
dataset generators to test scalability and dimensionality reduction techniques under
controlled conditions [21].
4.2 Data Preprocessing
To ensure the datasets were suitable for analysis, the following preprocessing techniques
were applied:
– Data Cleaning: Missing values were handled using k-nearest neighbors (kNN) imputa-
tion, while outliers were detected and removed using z-score analysis [5].
– Noise Reduction: Gaussian noise was filtered out using fuzzy logic-based smoothing
techniques [6].
– Normalization: Min-max scaling was applied to standardize feature ranges, enabling
consistent analysis across datasets with heterogeneous attributes.
4.3 Feature Engineering
Dimensionality reduction was performed to mitigate the curse of dimensionality and im-
prove computational efficiency:
– Principal Component Analysis (PCA): PCA was chosen for its ability to identify and
retain the most significant features while reducing redundancy [5].
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– Autoencoders: Deep learning-based autoencoders were employed to capture non-linear
feature relationships, further enhancing representation quality [10].
These techniques were selected to balance interpretability (PCA) and modeling power
(autoencoders), ensuring compatibility with the hybrid framework.
4.4 Evaluation Metrics
The performance of the proposed framework was assessed using the following metrics:
– Accuracy: Measures the proportion of correctly classified instances.
– Precision, Recall, and F1-Score: Evaluates the quality of predictions for imbalanced
datasets.
– Mean Absolute Error (MAE): Assesses the accuracy of continuous variable predictions.
– Processing Time: Tracks the time taken to complete tasks, particularly for real-time
applications.
– Scalability: Evaluates the framework’s ability to handle increasing data sizes and high-
dimensional datasets.
4.5 Scalability Tests
Scalability was a key focus of the experiments, and the following tests were performed:
– Data Volume Scaling: The framework was tested with datasets ranging from 1 GB to
100 GB to evaluate its ability to handle large-scale data.
– Dimensionality Scaling: High-dimensional synthetic datasets with feature sizes ranging
from 10 to 10,000 were used to assess the impact of dimensionality reduction techniques.
– Real-Time Data Streams: Streaming data was simulated using Apache Kafka, and the
framework’s latency and throughput were measured under varying load conditions.
4.6 Computational Environment
The experiments were conducted in a distributed computing environment to simulate
real-world conditions:
– Hardware: A cluster of 10 nodes, each equipped with Intel Xeon processors, 64 GB
RAM, and NVIDIA Tesla GPUs for accelerated computation.
– Software: Apache Spark for distributed processing, TensorFlow and PyTorch for ma-
chine learning tasks, and Scikit-learn for feature selection and evaluation.
– Operating System: Ubuntu 20.04.
– Tools and Libraries: Jupyter Notebook for interactive experimentation, Matplotlib and
Seaborn for visualization, and HDFS for data storage.
4.7 Comparative Methods
The proposed framework was compared against several baseline and state-of-the-art meth-
ods:
– Baseline Approaches: Traditional methods such as logistic regression, support vector
machines (SVM), and k-means clustering.
– Advanced Methods: Deep neural networks (DNN), genetic algorithms (GA), and stan-
dalone fuzzy logic systems.
– Hybrid Methods: Other hybrid frameworks combining neural networks with evolution-
ary algorithms or fuzzy logic.
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4.8 Experimental Procedure
The experiments were performed in three stages:
1. Preprocessing: Data cleaning, noise reduction, and normalization were applied to all
datasets to ensure consistency.
2. Feature Engineering: Dimensionality reduction techniques, such as PCA and autoen-
coders, were applied to reduce high-dimensional data.
3. Model Training and Testing: The framework was trained and tested on each dataset
using an 80/20 split for training and validation. Cross-validation was employed to
ensure robustness.
4.9 Hypotheses
The following hypotheses were tested:
– The hybrid framework improves accuracy and F1-score compared to baseline and ad-
vanced methods.
– The framework demonstrates superior scalability, maintaining performance as dataset
size and dimensionality increase.
– Real-time processing capabilities outperform existing methods in terms of latency and
throughput.
The results of the experiments, along with a detailed analysis, are presented in the
following section.
5 Results and Discussion
This section presents the results of the experiments conducted to evaluate the proposed
hybrid framework, followed by a discussion of its performance compared to baseline and
state-of-the-art methods. The analysis focuses on accuracy, scalability, processing time,
and real-time adaptability.
5.1 Evaluation Results
Accuracy and Prediction Performance The proposed framework outperformed base-
line and advanced methods across all datasets, as shown in Table 1.
Table 1. Accuracy and F1-Score Comparison Across Datasets
Dataset Method Accuracy (%) F1-Score
Healthcare Proposed Framework 94.5 0.92
Advanced DNN 89.3 0.85
Logistic Regression 74.8 0.68
IoT Sensor Data Proposed Framework 91.8 0.89
Hybrid Method 88.4 0.84
K-Means Clustering 71.5 0.62
Social Media Proposed Framework 90.2 0.87
Advanced DNN 86.7 0.81
SVM 78.3 0.74
Key Observations:
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– The proposed framework consistently achieved higher accuracy and F1-scores, partic-
ularly in noisy datasets such as Healthcare and IoT sensor data.
– Advanced methods like DNNs performed well but struggled with noisy and high-
dimensional data, highlighting the robustness of the hybrid framework.
Scalability Performance The framework’s scalability was evaluated by varying the
dataset size and dimensionality. Figure 3 illustrates the processing time as the dataset size
increases.
Fig. 3. Processing Time vs. Dataset Size for Scalability Tests
Key Observations:
– The proposed framework demonstrated near-linear scalability, with minimal increase
in processing time as dataset size grew from 1 GB to 100 GB.
– The integration of distributed computing platforms (e.g., Apache Spark) and particle
swarm optimization enabled efficient handling of large datasets.
Real-Time Processing Performance Real-time performance was assessed using sim-
ulated streaming data. Table 2 shows the latency and throughput comparisons.
Table 2. Real-Time Processing Performance
Method Average Latency (ms) Throughput (records/sec)
Proposed Framework 45 1200
Advanced Method 67 850
Baseline Method 105 520
Key Observations:
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– The hybrid framework achieved the lowest latency and highest throughput, demon-
strating its adaptability to real-time data streams.
– Swarm intelligence contributed significantly to resource optimization, enabling dy-
namic adaptation to varying workloads.
5.2 Discussion
Strengths of the Proposed Framework The experimental results highlight several
strengths of the proposed framework:
– Accuracy and Robustness: The integration of adaptive neuro-fuzzy systems and deep
neuro-evolution ensured high performance on noisy, high-dimensional datasets.
– Scalability: The near-linear scalability achieved during large dataset processing demon-
strates the framework’s ability to handle Big Data effectively.
– Real-Time Adaptation: The low latency and high throughput achieved in real-time
processing scenarios underline the framework’s suitability for dynamic environments.
Limitations and Future Work While the framework demonstrates significant advan-
tages, certain limitations were identified:
– The computational cost of deep neuro-evolution increases with the size and complexity
of the datasets.
– The framework’s reliance on distributed computing platforms may not be feasible in
resource-constrained environments.
Future work will focus on optimizing the computational efficiency of the framework
and exploring lightweight implementations for edge computing applications.
6 Conclusion and Future Work
This study introduced a hybrid framework for Big Data analytics, integrating artificial in-
telligence (AI), machine learning (ML), and advanced soft computing techniques to address
critical challenges in the field. By tackling issues such as scalability, high-dimensionality,
uncertainty, real-time processing, and data integration, the framework demonstrated its
potential to enhance robustness, adaptability, and efficiency. Experimental results showed
that the framework consistently outperformed baseline and advanced methods across var-
ious datasets and evaluation metrics. Notable contributions include the integration of
adaptive neuro-fuzzy systems and deep neuro-evolution, which ensured high accuracy and
interpretability, even for noisy and high-dimensional datasets. Furthermore, the frame-
work achieved near-linear scalability on large datasets, low latency, and high throughput
in real-time scenarios, demonstrating its suitability for dynamic environments. Applica-
tions in domains such as healthcare, IoT, and social media analytics underscored the
framework’s versatility and practical utility.
While the proposed framework addresses many challenges, there remain opportunities
for further research and development. Future work will focus on optimizing the computa-
tional cost of deep neuro-evolution by exploring parallelized and lightweight implementa-
tions. Deploying the framework in edge computing environments will be investigated to en-
able real-time processing closer to the data source, reducing latency and bandwidth usage.
Incorporating automated feature engineering techniques, such as reinforcement learning-
based feature selection, could further improve efficiency and scalability. Additionally, ex-
tending the framework to handle diverse data types, including video, audio, and graphs,
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would broaden its applicability. Enhancing explainability through visualization tools and
interpretable models is another priority, particularly for critical applications in healthcare
and finance. Finally, exploring energy-efficient implementations will align the framework
with the goals of sustainable computing.
In conclusion, the proposed framework offers a robust and scalable solution to the
evolving challenges of Big Data analytics. By addressing current limitations and expanding
its capabilities, this research lays the groundwork for next-generation analytical systems
capable of unlocking the full potential of Big Data across diverse industries.
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Authors
Praveen Kumar Myakala is a lifelong learner with a passion
for innovation and education. A Master’s graduate in Data Science
from Colorado Boulder University, he has published impactful re-
search and strives to inspire others through writing and mentoring.
Committed to transformative learning and creative exploration, he
advocates for balancing professional growth with personal fulfill-
ment while focusing on creating meaningful solutions.
Anil Kumar Jonnalagadda has over 17 years of experience in
Data Engineering, having contributed to leading companies such
as Amazon, Google, Meta, and Oracle. He specializes in designing
large-scale data pipelines, real-time analytics, and predictive models
using neural networks and evolutionary algorithms. His expertise in-
cludes developing intelligent systems for resource optimization and
anomaly detection in Big Data. His current interests lie in AI, Ma-
chine Learning, Robotics, and Neural Networks.
Prudhvi Naayini is a highly accomplished Infrastructure Engineer
with over 20 years of experience at global organizations like JPMor-
gan Chase, AT&T, and Unisys. With a Master’s degree from Osma-
nia University, he specializes in performance engineering, HPC, and
integrating AI/ML workloads in cloud environments. Currently, he
is expanding his expertise in AI/ML and engineering management
to stay ahead in the evolving tech landscape.
13

Revolutionizing Big Data with AI-Driven Hybrid Soft Computing Techniques

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