Srinivas Aluvala

EDUCATION

M.Tech (CSE)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Engineering, Artificial Intelligence, Computer Science Applications

Scopus Publications

Efficient spectrum sharing in 5G and beyond network: a survey
Akhil Gupta, Raabia Kausar, Sudeep Tanwar, Abdulatif Alabdulatif, Vrince Vimal, and Srinivas Aluvala
Springer Science and Business Media LLC



Mountain Gazelle Optimization Based Energy Efficient Cluster Head Selection and Routing Protocol for Internet of Things
Srinivas Aluvala, Abbas Hameed Abdul Hussein, Siva Surya Narayana Chintapalli, Satya Prakash Singh, and Vijaya Lakshmi Sarraju
Springer Nature Singapore



Characterization of Destructive Nodes and Analysing their Impact in Wireless Networks
Srinivas Aluvala and V. Srikanth
International Journal of Computational and Experimental Science and Engineering
Mobile Ad hoc Networks (MANETs) are being used to meet new requirements for efficiency and coordination in a variety of new public and residential contexts. Certain essential functions, including as resource management among network nodes, trust-based routing, and security for network maintenance, are not performed as well as they should because of the dynamic nature of wireless networks. Ad-hoc networks can also be attacked from different tiers of a network stack, and they are susceptible to secure communications. Destructive nodes have the ability to alter or reject routing parameters. They may also provide bogus routes in an attempt to intercept source data packets and pass them through. To handle the complexity arising from secure data exchange, some protocols have been developed. However, not all attack types can be detected and eliminated by a secure protocol in every scenario. Since security is not a feature that is built into MANETs, new secure wireless protocols need to concentrate on these issues. Thus, the analysis of destructive nodes' characteristics and effects on wireless networks in this research paper examined the behaviour of multiple attacks, their activities through neighbour selection, the establishment of paths from sources to destinations, and the dissemination of attack presence detection information to regular devices during path discovery and data transmission mechanisms. In order to categorize as legitimate, nodes must be constructed with safe transmission knowledge to provide trustworthy communication, validation, honesty, and privacy.


PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes
Veluru Chinnaiah, Vadlamani Veerabhadram, Ravi Aavula, and Srinivas Aluvala
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
We proposed a deep learning-based process mining framework known as PMiner for automatic detection of anomalies in business processes. Since there are thousands of business processes in real-time applications such as e-commerce, in the presence of concurrency, they are prone to exhibit anomalies. Such anomalies if not detected and rectified, cause severe damage to businesses in the long run. Our Artificial Intelligence (AI) enabled framework PMiner takes business process event longs as input and detects anomalies using a deep autoencoder. The framework exploits a deep autoencoder technique which is well-known for Its ability to discriminate anomalies. We proposed an algorithm known as Intelligent Business Process Anomaly Detector (IBPAD) to realize the framework. This algorithm learns from historical data and performs encoding and decoding procedures to detect business process anomalies automatically. Our empirical results using the BPI Challenge dataset, released by the IEEE Task Force on Process Mining, revealed that PMiner outperforms state-of-the-art methods in detecting business process anomalies. This framework helps businesses to identify process anomalies and rectify them in time to leverage business continuity prospects.


Cyber security: Role of social media in mitigation of various forms cyber crimes
Goli Sunil, Srinivas Aluvala, Chinthala Sujitha, Akarapu Mahesh, Areefa, Kanegonda Ravi Chythanya, and Gadde Aruna
AIP Publishing



A review on impact of cyber crimes
Vishali Sivalenka, Srinivas Aluvala, Khaja Mannanuddin, G. Sunil, J. Vedika, and V. Pranathi
AIP Publishing



Enhancing Tourism Experiences Through Immersive Technologies: The Role of Virtual and Augmented Reality
Sanchit Vashisht, Bhanu Sharma, and Srinivas Aluvala
IEEE
This study examines the influence of immersive technologies, particularly Virtual Reality (VR) and Augmented Reality (AR), on the tourism industry, emphasizing recent developments and the obstacles encountered in their integration. VR and AR deliver transforming experiences through virtual tours, interactive guides, and augmented on-site information, so redefining the marketing, exploration, and enjoyment of destinations. Current systems encounter obstacles like elevated development expenses, technical intricacies in attaining realistic and engaging user experiences, and constraints in accessibility and scalability across many platforms. This study emphasizes cutting-edge technologies such Unity3D, Vuforia, and Blender 3D, elucidating their functions in the creation of a VR and AR application aimed at enhancing tourism experiences. A framework is designed to address these problems, delivering scalable, user-friendly, and visually immersive applications that may establish new benchmarks for accessibility and environmental sustainability in tourism. The findings illustrate the potential of VR and AR to enhance travel experiences by making them more engaging, accessible, and sustainable, highlighting the necessity for continuous study and innovation to fully realize their capabilities within the tourism sector.


Interactive Carpentry Training with Augmented Reality Technology
Kishan Poddar, Bhanu Sharma, and Srinivas Aluvala
IEEE
This research study presents a markerless augmented reality (AR) application that aims to deliver basic carpentry knowledge in an engaging, interactive, and informative learning experience. The application offers users to project the 3D model animations of different necessary carpentry hand tools like hacksaw, carpenter's glue, and hand plane into real-world spaces. Using a simulated but realistic context, user can visualize and practice appropriate tool usage which significantly helps to understand and learn faster. User testing showed that the app increased engagement and improved learning results when compared to traditional methods. The complete outcomes illustrate AR's power in some vocational training. It provides a very practical and affordable way to learn by doing. Future work will include scaling up the tool library and developing supported collaboration to deepen learning processes and extend interaction into more intricate aspects of carpentry.


Internet of Things Enabled Automatic Meter Reading
Sarthak Malik, Shikha Agarwal, Srinivas Aluvala, Jyoti S. Bali, Rachit Garg, and Mohemmed Hussien
IEEE
The world is currently going through a huge transition towards digitalization and sustainability. Sustainability focuses on responsible resource management and environmental preservation, whereas digitalization entails the conversion of many processes and systems into connected, digital forms. Energy management, which aims to offer clean and affordable energy solutions while minimising waste and environmental damage, is one of the important sectors garnering significant attention in this context. Energy management involves the methodical optimisation of energy use in a variety of contexts, including commercial and industrial facilities and residential settings. The energy metre, a tool used to measure electricity, gas, or other energy kinds, is essential to this project. Traditional energy metres are evolving into "smart" energy metres, which may send consumption data periodically or in real-time in addition to measuring energy usage. The usability of energy metres is considerably increased by this feature for both consumers and energy suppliers. Industry 4.0 refers to the fourth industrial revolution, which is characterised by the incorporation of digital technologies into manufacturing and industrial processes, in the broader context of business and technology. Internet of Things (IoT), artificial intelligence (AI), and automation are key technologies advancing this transformation. These technologies are essential for improving productivity, sustainability, and efficiency across sectors. A key component of increasing energy management practises is the Internet of Things (IoT). The internet of things (IoT) is a network of connected gadgets and sensors that can gather and send data. IoT-enabled devices are crucial to the real-time monitoring and management of energy use in the field of energy management. These gadgets are capable of collecting information from many sources, such as energy metres, and transmitting it for analysis and well-informed decision-making. An IoT-improved Automatic Metre Reading (AMR) system is shown in the study. Energy metres can be remotely read using AMR technology, eliminating the requirement for manual intervention. In this case, IoT technology enhances the capabilities of the AMR system by being easily incorporated into it. Wireless sensors and specialised communication protocols are used in the energy metres as part of this integration to enable real-time data monitoring and transfer.


Law Enforcement and Dispensation of Judicial Equipoise: Convergence of Artificial Intelligence in Administration of Justice
Pallavi Raj, Poonam Rawat, Jitendra Singh, Shweta Pandey, Srinivas Aluvala, and Vikrant Pachouri
IEEE



Revolutionizing Tuberculosis Treatment: Smart Technology Innovations for Mycobacterium tuberculosis Management
Atreyi Pramanik, Ajay Singh, Gouri Rani, Shival Dubey, and Srinivas Aluvala
IEEE
Tuberculosis remains a significant global problem. The modification in smart technology (ST) invention generates novel chances to reform tuberculosis (TB) controlling. This work explores the potential of ST for filtering MTB diagnostics and therapy. ST includes telemedicine, mobile health uses (mHealth), wearable devices (WD) and data analytical that deliver many options of improving TB control. Telemedicine licenses distant consulting thus empowering medics to access patients in remote areas, offer quick detects as well as recommend treatment. mHealth apps certify that individuals have right of entry to education materials, can remind them when it’s time for medication and offers self-monitoring tools which enhance adherence to the therapy by keeping the patient involved during the course of treatment. WD support real-time (RT) monitoring of heart rates, blood pressure level, physical activity and other vital signs that are important in coming up with modified remedy plans while also detecting response to cure earlier enough. Data analytics combined with AI can examine large amounts of patient data to identify patterns, forecast drug effects and optimize treatment strategies resulting in more efficient individualized care. These novelties through intellectual technologies may be used as solutions for tasks experienced during TB management, leading to improved patient outcomes and a reduction in the TB burden.


AI Technological advancements in the design of intangible cultural products and Preservation of Cultural Heritage Sites
Priyam Agarwal, Siddharth Swami, Mohammed Ismail Iqbal, Divya Rawat, Lalit Mohan Joshi, and Srinivas Aluvala
IEEE
Culture plays a significant role in shaping audience’s behavior, especially in this digitalized era, where large portion of any individual’s day is spent online resulting in building global integration of people belonging to different cultures. Therefore, Cultural Products and Heritage Sites are also equally important as it holds a major part of any country’s glorious and rich history and can earn recognition at a global level for any country. The main aim of this paper was to identify latest advancements in 4.0 enabling technologies such as Artificial Intelligence (AI), Virtual Reality (VR) and big data in innovatively digitally designing intangible cultural products and measures for ensuring long term sustainability of Cultural Heritage Sites (CHS). This paper has also given some future recommendations for the smooth management and establishing creative digital data base set design for CHS.


Real-Time Crop Monitoring System Using Deep Learning Techniques for Increasing Production
Chiranjit Dutta, Suveg Moudgil, Srinivas Aluvala, Virat Raj Saxena, K. Devi, and Jagendra Singh
IEEE
This study applies machine learning to determine rice crop production using sensor information from temperature, humidity, and water levels. This project looks forward to providing insights to maximize agricultural practices and allow data-driven decision-making in rice cultivation. This is in line with how well these disparate machine learning models can predict rice crop production when measured against precision, accuracy, recall, and F1 score. To some extent, this is evident, as this study found that the machine learning models can predict yield quite satisfactorily, of which the model Random Forest performs very well in terms of precision and accuracy. Among them were the exactitude of sensor data and the algorithm selection, along with subsequent model assessment, refinement, and the ingression of great datasets inclusive of a broader set of environmental characteristics and crop yields—that were considered to be crucial in improving the precision of yield prediction algorithms. Additionally, the use of machine learning in farming has a number of benefits, such as enabling farmers to make decisions about crop management, irrigation, and optimization of yield. Combining machine learning methodologies with sensor technology can optimize resource allocation, save water, and improve overall agricultural productivity. Further, this study will facilitate the implementation of smart irrigation systems and set the stage for data-driven decision-making in agriculture for better farming practices and food production worldwide.


Sign Language Recognition and Translation Using Self-Attention Long-Short-Term Memory with Shape Autotuning Activation Function
Keerthi Kumar M, Parameshachari B D, Kay Hooi Keoy, Srinivas Aluvala, and Ammar Hameed Shnain
IEEE
An efficient Indian Sign Language recognition (ISL) identifies the sign language gestures to ease communication among the non-signer and signer community. It is a graphic form of communication where the community of hearing-impaired exists and this sign language raises independently from local spoken language. However, sign language recognition and translation face constraints due to variability in sign production between individuals and contextual ambiguity which makes it difficulties for models to generalize and accurately interpret signs in various contexts. This research proposes Self-Attention Long-Short-Term Memory with Shape Autotuning Activation Function (SALSTM-SAAF) to recognize and translate sign language. RA allows the model to focus on essential parts and LSTM captures temporal dependencies which ensures effective sequence learning. SAAF adjusts to different input shapes which optimizes the learning process and enhances the model’s performance. Data augmentation and min-max normalization are applied to increase the dataset size and normalize the obtained data. Then, the ResNet is used to extract the features from pre-processed data effectively. The SALSTM-SAAF achieves a better accuracy of 99.87% compared to existing methods like VGG19-Bidirectional LSTM (VGG19-BiLSTM).


Particle Swarm Optimization based Prediction and Ensemble Machine Learning Classification of Wildlife Habitats
E G Satish, Srinivas Aluvala, Hassan M. Al-Jawahry, G Vasukidevi, and M. Surya Bhupal Rao
IEEE
The accurate classification of wildlife habitats is necessary as it provides necessary resources and condition of wildlife population. To improve this prediction task, the proposed research developed Machine Learning (ML) framework using the remote sensing data. The data is initially pre-processed using Adaptive Histogram Equalization (AHE) and the features such as spectral, special, and texture features are extracted. The features are then selected using Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The features are then optimised using Particle Swarm Optimization (PSO) for optimal prediction and then classified using Ensemble Machine Learning (EML) algorithms such as Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). The method showed better capabilities to classify different kinds of wildlife habitats and the values of integrating optimization methods with EML algorithms for solving ecological problems. The experimental results showed that the proposed PSO-EML algorithm achieved better classification accuracy and optimized prediction with an accuracy of 99.61%, and precision of 97.39% when compared to the existing methods Boosted Regression Tree (BRT) and Hierarchical clustering in handling high dimensions and noise features of ecological data.


A Web-Based Approach for Malaria Parasite Detection Using Deep Learning in Blood Smears
Srinivas Aluvala, Keshoju Bhargavi, Jula Deekshitha, Banoth Suresh, Gujja Nitesh Rao, and Athirajula Sravani
IEEE
Malaria affects public health issues significantly and is one of the most severe infectious diseases in the world. Anopheles mosquitoes attack humans who carry the virus in order to disseminate it. To manage the sickness and get the best potential treatment outcomes, accurate parasite identification is essential. A critical first step in the diagnosis and treatment of malaria is the traditional method of using a microscope to search blood samples for malarial parasites. A diagnosis made using this approach is time-consuming since it relies heavily on the examiner's expertise and experience. To improve the speed and accuracy of diagnosis, this study suggests a deep learning model for malarial parasite prediction. In this study, we report on a Convolutional Neural Networks (CNN) model, also called the VGG-19 model, which detects malaria parasites with 97% accuracy using microscopic images of blood samples. Enhancing the efficacy and precision of the diagnosis is the aim of this method. This model has been trained on a set of images of blood smears and is capable of accurately distinguishing between samples that are infected and those that are not. Malaria may be less common in areas where it is endemic if this automated diagnostic method is successfully implemented and results in early diagnosis and treatment.


Weed Recognition Using Image Patches Based Global Hybrid Attention with Densenet-169 Model
Hima Bindu Valiveti, Muntather Almusawi, E G Satish, Srinivas Aluvala, and E S Challaraj Emmanuel
IEEE
Weeds are one of the main problem in agriculture that affect the crop yield. Accurate classification and recognition is major challenges in weeding because of same visual characteristics between plants and weeds. To solve this problem, in this research patch based image recognition is integrated with the Deep Learning (DL) approach to improving the classification and recognition of weeds. The weed image data are collected from the Deepweeds dataset. For pre-processing Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is utilized for change the image size and then generated to small patches. Selection of relative important patches are performed by Fast Fourier Transform (FFT) and Laplacian Filter (LF) are trained to the DL model. The proposed Global Hybrid Attention mechanism with Densenet-169 model (GHA-Densenet-169) employed for classification of weeds with respective classes. The evaluation results of the proposed method using performance metrics are Accuracy, Recall, Precision and F1-score. The proposed approach attained high accuracy of 99.28%, recall of 99.26%, precision of 99.27% and f1-score of 99.25% which is greater than existing deep learning models such as Densenet-201, VGG-SVM, Adapting MobilenetV2, Resnet-SVM, and Swin Transformer and Two-stage Transfer Learning (ST-TSTL).


Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection
Mohanarangan Veerappermal Devarajan, Srinivas Aluvala, Vinaye Armoogum, S. Sureshkumar, and H T Manohara
IEEE
The Industrial Internet of Things (IIoT) is experiencing rapid growth, and robust cyber security measures to protect against from the cyber-attacks with the help of Anomaly Detection System (ADS) and signature-based detection system. The sensors collect vast amount data from the environment, presenting functionality challenges for device. To overcome this problem, many Network Intrusion Detection Systems (NIDS) had developed for secure to IIoT systems. But NIDS faces challenges due to complexity of information collection required for threat detection. So, propose study work introduces a Recurrent rule-based Feature Selection (RFS) for IIoT system. NSL-KDD and UNSW-NB15 dataset are used for performing operation and relevant features are selected by using hybrid rule-based algorithm. Then RFS model classifies the data and predict the attacks. the proposed method performance is superior than existing method and better results in terms of accuracy rates of 99.0% and 98.9%, detection rates of 99.0% and 99.9%, and low false positive rates of $1.0 \\%$ and $1.1 \\%$ respectively.


Bankruptcy Detection Using Random Forest Classifier and Its Data Balancing Using Smote Analysis
Nishant Pritam, Sonal Malhotra, Srinivas Aluvala, Kanwarpartap Singh Gill, and Swati Devliyal
IEEE
This research examines and access the efficacy of the Random Forest Classifier in precisely detecting the likelihood of bankruptcy in financial datasets. Additionally, it investigates the use of Synthetic Minority Over-sampling Technique (SMOTE) to address the widespread issue of data imbalance in bankruptcy prediction. This research examines the effectiveness of the Random Forest model in detecting bankruptcy and evaluates the impact of SMOTE in enhancing the accuracy of classification. The findings suggest that Random Forest has significant promise in forecasting bankruptcy, while the use of SMOTE to address imbalanced data yields a favourable effect, bolstering the dependability of financial risk assessment models. An accuracy rate of $\\mathbf{9 7 \\%}$ is reached by taking into account a diverse variety of optimisation parameters. Future study on bankruptcy detection may have significant ramifications and applications across several industries. Subsequent investigations may prioritise enhancing the precision and promptness of bankruptcy prediction models. Financial institutions, investors, and enterprises would find it advantageous to use this approach in order to effectively handle and reduce financial risks linked to prospective bankruptcies. To summarise, future research on bankruptcy identification has the potential to contribute to breakthroughs in financial risk management, economic analysis, regulatory compliance, corporate governance, and the ethical use of artificial intelligence. This has the capacity to provide significant knowledge and resources for individuals and organisations in many industries and sectors.


Convolutional Neural Network with Convolutional Block Attention Mechanism for Fingerprint Forgery Detection
Muntather Almusawi, Srinivas Aluvala, S Trisheela, Mukesh Soni, and Revathi. R
IEEE
The detection of fingerprint liveness has affected through spoofing, that is the major threat for fingerprint-based biometric systems. The issue of forgery detection is well studied and forged fingerprints gives huge impact of outcomes in biometric depended on security systems. In this research, the Convolutional Neural Network (CNN) with Convolutional Block Attention Mechanism (CBAM) for the detection of forgery in fingerprint images. The dataset used for this research are LivDet-2013 and LivDet-2015 and it is pre-processed by using Circular Hough Transform (CHT) method. Then, the features are extracted by using the Local Binary Pattern (LBP) method that extracts the meaningful features. The detection and classification are performed by using CNN with CBAM method that focuses much on detected patterns and detected the forgery with high accuracy. The proposed CNN with CBAM method attained 98.12% accuracy on LivDet-2013 and 97.05% accuracy on LivDet-2015 datasets while compared to existing methods like Hybrid Fingerprint Presentation Attack Detection (HyFiPAD).


Avoidance of Ship Collision Using Deep Reinforcement Learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces
Revatthy Krishnamurthy, Myasar Mundher Adnan, Sunil Kumar V, T. Aditya Sai Srinivas, and Srinivas Aluvala
IEEE
Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement Learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values.


Integration of Mobile Edge Computing in Wireless Technology
Prantik Kumar Mahata, Mukul Jain Saklecha, Sushruta Mishra, Hrudaya Kumar Tripathy, Biswajit Brahma, Rajeev Sobti, and Srinivas Alluvala
Springer Nature Singapore



Revolutionizing Accounting with Blockchain Technology for Enhanced Security and Efficiency
Vasim Ahmad, Madhu Arora, Rakesh Kumar, Srinivas Aluvala, Ashish Vishnoi, and Lalit Goyal
IEEE
In recent years, blockchain technology has become more popular in many fields as it can make things safer and efficient. In future, blockchain technology could be very helpful for the financial field. Accounting is an important part of any business, and the success of the business depends on how accurate and reliable it is. On the other hand, traditional accounting methods are slow and prone to mistakes that can cost a lot of money. Blockchain technology has the potential to change the accounting field by making it safer, open and efficient. People have said that the blockchain technology is a revolutionizing technology that can change many fields, including accounting. Cryptocurrencies like Bitcoin use a technology called decentralized and distributed ledger, which could be used to make financial systems safer and more efficient. This study will discuss about how blockchain technology could change accounting and what benefits it might have.


An Advanced Container Tracking System for Real-Time Monitoring and Automated Alerting of Container Security and Logistics
Siddhi Nath Rajan, Preeti Sharma, Deepti Srivastava, Kanchan Koul, Srinivas Aluvala, and Shashikant
IEEE
The effectiveness and security of logistics operations are crucial to the management of supply chains and the success of organizations in today's linked, globalized environment. Transporting goods, especially inside heavy-duty containers, calls for accuracy as well as increased security measures to protect priceless assets while in transit. The Advanced Container Tracking System is a novel response to these urgent needs. This technology provides real-time monitoring and automatic notifications in order to strengthen container security and enhance logistics. Supply chain management has always relied heavily on container monitoring to guarantee the timely and secure delivery of commodities. Traditional approaches frequently relied on manual tracking or recurrent updates, which could result in errors and security flaws. In response, the suggested Advanced Container Tracking System makes use of cutting-edge technologies to deliver a reliable, comprehensive tracking solution. The combination of these technologies makes continuous real-time position tracking and data storage possible, guaranteeing that container movements are continually documented and easily accessible for analysis and security needs. Additionally, the system has cloud connectivity, allowing authorized stakeholders to access real-time container data using a specially developed mobile application that can be used whenever and wherever they choose. System performance is enhanced through algorithm optimization, the application of data compression techniques, and consideration of hardware upgrades. The system's capacity to spot and address irregularities in container movement is one of its standout features. When a container stays still for longer than expected and crosses certain thresholds, the system will immediately send alerts to the owner or authorized administrators. This proactive strategy might potentially reduce risks associated with unauthorized stops or unexpected delays while also adding an additional degree of security. The proposed system acquires real-time latitude and longitude coordinates through continuous GPS updates, temporarily storing this data in a buffer and periodically saving it to the SD card for local storage. Here, are the technical details of the Advanced Container Tracking System in this document, giving a thorough explanation of its architecture, parts, and operating principles. Additionally, this innovation integrates cloud technology and creates a user-friendly mobile app for real-time monitoring. In addition, the investigate the advantages and possible uses of the Advanced Container Tracking System for boosting container security, raising logistics efficiency, and lowering operating hazards. Establishing secure cloud connectivity with HTTPS, implementing robust authentication for the mobile application, and applying data encryption for both stored data on the SD card and during transmission contribute to the system's security. The major objective of introducing this cutting-edge container tracking system is to promote supply chain and logistics management. The goal is to provide a complete solution that not only tackles operational efficiency issues but also security issues. This is thoroughly analyzed in the parts that follow in this article, covering installation information, test results, and the potential for its use in various logistics scenarios.


Examining Internet-based Hate Crime Through Artificial Intelligence
Sagar Saxena, Anil Kumar Dixit, Shweta Pandey, Vikrant Pachouri, Srinivas Aluvala, and Ashima Juyal
IEEE
Internet has emerged as a platform for propagating hatred. This study examines the relationship between the usage of social networking sites and hate crimes. The research findings imply that social media may serve as a channel for the spread of hated digital information and violent behaviors. As well as the usage of Machine learning, which is a subfield of AI to the study of hate crimes and the elements that lead to them on the internet. Further in this study authors in this paper investigate the causes of hate crimes and the elements that contribute to their development. Because of their unique significance, hate crimes require consideration. Crimes motivated by prejudice intimidate the survivor and the victim's society. That is why it necessary to investigate the side of protection and prevention policies of international laws that are given to be implemented by the different international bodies. The article's methodology is based on an extensive review of the literature, study of websites through them the researchers examine importantly, fighting hate crimes requires first monitoring and analyzing their dynamics to properly comprehend their nature. Since the propagation of hateful communication can be an early indicator of violence, especially heinous crimes, curbing hate crime could help minimize its effects. Thus, this paper addresses the targets of hatred on the Internet, presents a framework through which issues may be detected and remedied by emphasizing moral and social responsibility, and outlines potential legislatives to counteract this growing scourge on the Internet.

RECENT SCHOLAR PUBLICATIONS

Utilization of DenseNet201, EfficientNetB3, Resnet50, and VGG19 as Pre-Trained Convolutional Neural Network Models for Brain Tumour Classification
KS Gill, R Gupta, G Shandilya, S Aluvala, A Sulaiman, H Alshahrani, ...
2025 International Conference on Artificial Intelligence in Information and 2025



Towards Sustainable Agriculture: Mango Leaf Disease Classification with Deep Learning Models
S Aluvala, S Chauhan
2025 3rd International Conference on Intelligent Data Communication 2025



Interactive Carpentry Training with Augmented Reality Technology
K Poddar, B Sharma, S Aluvala
2024 9th International Conference on Communication and Electronics Systems 2024



Enhancing Tourism Experiences Through Immersive Technologies: The Role of Virtual and Augmented Reality
S Vashisht, B Sharma, S Aluvala
2024 9th International Conference on Communication and Electronics Systems 2024



Random Coupled Neural Network with Sand Cat Swarm Optimization for Automatic Object Detection in Aerial Images
S Kaliappan, B Sinha, M Ramya, S Aluvala, R Maranan
2024 IEEE 4th International Conference on ICT in Business Industry 2024



A Deep Learning Model for Automated Kidney Disease Diagnosis Using CT Scan Images
S Aluvala, PS Rao, K Bhargavi, V Sivalenka
2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 1-6 2024



Particle Swarm Optimization based Prediction and Ensemble Machine Learning Classification of Wildlife Habitats
EG Satish, S Aluvala, HM Al-Jawahry, G Vasukidevi, MSB Rao
2024 International Conference on Intelligent Algorithms for Computational 2024



Sign Language Recognition and Translation Using Self-Attention Long-Short-Term Memory with Shape Autotuning Activation Function
BD Parameshachari, KH Keoy, S Aluvala, AH Shnain
2024 International Conference on Intelligent Algorithms for Computational 2024



Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection
MV Devarajan, S Aluvala, V Armoogum, S Sureshkumar, HT Manohara
2024 Second International Conference on Networks, Multimedia and Information 2024



Weed Recognition Using Image Patches Based Global Hybrid Attention with Densenet-169 Model
HB Valiveti, M Almusawi, EG Satish, S Aluvala, ESC Emmanuel
2024 Second International Conference on Networks, Multimedia and Information 2024



Avoidance of Ship Collision Using Deep Reinforcement Learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces
R Krishnamurthy, MM Adnan, S Kumar, TAS Srinivas, S Aluvala
2024 International Conference on Data Science and Network Security (ICDSNS), 1-4 2024



Convolutional Neural Network with Convolutional Block Attention Mechanism for Fingerprint Forgery Detection
M Almusawi, S Aluvala, S Trisheela, M Soni
2024 International Conference on Data Science and Network Security (ICDSNS), 1-4 2024



A Web-Based Approach for Malaria Parasite Detection Using Deep Learning in Blood Smears
S Aluvala, K Bhargavi, J Deekshitha, B Suresh, GN Rao, A Sravani
2024 2nd World Conference on Communication & Computing (WCONF), 1-6 2024



PMiner: Process mining using deep autoencoder for anomaly detection and reconstruction of business processes
V Chinnaiah, V Veerabhadram, R Aavula, S Aluvala
International journal of electrical and computer engineering systems 15 (6 2024



Bankruptcy Detection Using Random Forest Classifier And Its Data Balancing Using Smote Analysis
N Pritam, S Malhotra, S Aluvala, KS Gill, S Devliyal
2024 OPJU International Technology Conference (OTCON) on Smart Computing for 2024



A review on impact of cyber crimes
V Sivalenka, S Aluvala, K Mannanuddin, G Sunil, J Vedika, V Pranathi
AIP Conference Proceedings 2971 (1) 2024



Cyber security: Role of social media in mitigation of various forms cyber crimes
G Sunil, S Aluvala, C Sujitha, A Mahesh, A Areefa, K Ravi Chythanya, ...
AIP Conference Proceedings 2971 (1) 2024



A Semiconductor Manufacturing Final Test Yield Classification Using Random Forest
R Dineshkumar, S Aluvala, S Srinath, Z Alsalami, VT Krishnaprasath
2024 Second International Conference on Data Science and Information System 2024



Dynamic Resource Allocation-Enabled Distributed Learning as a Service for Vehicular Networks
T Ganesan, RR Al-Fatlawy, S Srinath, S Aluvala, RL Kumar
2024 Second International Conference on Data Science and Information System 2024



Law Enforcement and Dispensation of Judicial Equipoise: Convergence of Artificial Intelligence in Administration of Justice
P Raj, P Rawat, J Singh, S Pandey, S Aluvala, V Pachouri
2024 Parul International Conference on Engineering and Technology (PICET), 1-5 2024

MOST CITED SCHOLAR PUBLICATIONS

Confluence of Machine Learning with Edge Computing for IoT Accession
K Mannanuddin, S Aluvala, Y Sneha, E Kumaraswamy, E Sudarshan, ...
IOP Conference Series: Materials Science and Engineering 981 (4), 042003 2020
Citations: 67



A Novel Technique for Node Authentication in Mobile Ad hoc Networks
S Aluvala, KR Sekhar, D Vodnala
Perspectives in Science 8, 680-682 2016
Citations: 32



An Empirical Study of Routing Attacks in Mobile Ad-hoc Networks
S Aluvala, KR Sekhar, D Vodnala
Procedia Computer Science 92, 554-561 2016
Citations: 32



Concurrences of deep learning arise in analysis of bigdata
V Sivalenka, S Aluvala, Y Sneha, K Mannan, S Farheen, K Mahender
AIP Conference Proceedings 2418 (1) 2022
Citations: 31



Design a Cost Optimum for 5g Mobile Cellular Network Footing on NFV and SDN
BV Kumar, Y Chanti, N Yamsani, S Aluvala, B Bhaskar
International Journal of Recent Technology and Engineering (IJRTE) ISSN 2019
Citations: 30



IoT based saline level monitoring system
G Sunil, S Aluvala, GR Reddy, V Sreeharika, P Sindhu, S Keerthana
IOP Conference Series: Materials Science and Engineering 981 (3), 032095 2020
Citations: 18



An efficient backbone based quick link failure recovery multicast routing protocol
D Vodnala, SP Kumar, S Aluvala
Perspectives in Science 8, 135-137 2016
Citations: 14



Security Enhancement of Genome Sequence Data in Health Care Cloud
SSY G.Sunil, Srinivas Aluvala, Nagendar Yamsani, Kanekonda Ravi Chythanya
International Journal of Advanced Trends in Computer Science and Engineering 2019
Citations: 13



Dynamic Resource Allocation-Enabled Distributed Learning as a Service for Vehicular Networks
T Ganesan, RR Al-Fatlawy, S Srinath, S Aluvala, RL Kumar
2024 Second International Conference on Data Science and Information System 2024
Citations: 10



A traditional novel approach for skill enhancement of teaching-learning process in engineering education
S Aluvala, S Pothupogu
Journal of Engineering Education Transformations 28 (4), 92-95 2015
Citations: 10



Advancing bug detection in solidity smart contracts with the proficiency of deep learning
SMP Gangadharan, C Arya, S Aluvala, J Singh, P Singh, A Murugesan
2023 3rd International Conference on Innovative Sustainable Computational 2023
Citations: 9



Unlocking the power of natural language processing through journaling with the assistance
AK Mishra, KK Bhartiy, J Singh, S Aluvala, P Singh, K Kishor
2023 3rd International Conference on Innovative Sustainable Computational 2023
Citations: 9



Impact of experimental learning on graduates success in engineering education
Y Sneha, S Aluvala
Journal of Engineering Education Transformations 34, 666-669 2021
Citations: 9



Secure routing in MANETS using adaptive cuckoo search and entropy based signature authentication
S Aluvala, K Rajasekhar
Wireless Personal Communications 128 (3), 1519-1541 2023
Citations: 8



An improved load balancing in MANET using on-demand multipath routing protocol
N Yamsani, BV Kumar, S Aluvala, M Dandugudum, GS Reddy
International Journal of Engineering & Technology 7 (1.8), 222-225 2018
Citations: 8



Geetanjali and A
PK Malik, AS Duggal, S Aluvala, R Sahithi
Gehlot," Development of a low-cost IoT device using ESP8266 and Atmega328 2023
Citations: 7



Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection
MV Devarajan, S Aluvala, V Armoogum, S Sureshkumar, HT Manohara
2024 Second International Conference on Networks, Multimedia and Information 2024
Citations: 6



BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging
S Chauhan, R Cheruku, D Reddy Edla, L Kampa, SR Nayak, J Giri, ...
Frontiers in Physiology 15, 1349111 2024
Citations: 6



An efficient on-demand link failure local recovery multicast routing protocol
D Vodnala, S Phani Kumar, S Aluvala
Proceedings of 2nd International Conference on Intelligent Computing and 2017
Citations: 6



An empirical study of various face recognition and face liveness detection techniques and algorithms
V Sivalenka, S Aluvala, Y Sneha, K Mannan, S Farheen, E Kumaraswamy
AIP Conference Proceedings 2418 (1) 2022
Citations: 5