Artificial General Intelligence: Advancements, Challenges, and Future Directions in AGI Research Gokul Yenduri, Ramalingam Murugan, Praveen Kumar Reddy Maddikunta, Sweta Bhattacharya, Devulapalli Sudheer, Bharath Bhushan Savarala IEEE Access, 2025 Artificial General Intelligence (AGI) is a transformative shift in artificial intelligence that aims to match human-like reasoning, flexibility, and self-learning. Unlike Narrow AI, which is capable of performing only a limited set of tasks, AGI aspires to handle any intellectual task by exhibiting human-like learning, reasoning, and behavior. This enables AGI to offer extraordinary potential in various domains such as healthcare, education, transportation, and more. This paper provides a comprehensive review of the fundamental concepts, applications, and challenges associated with AGI’s development. This systematic review of the literature explored AGI’s transformative potential, from personalized healthcare and adaptive learning systems to advanced autonomous vehicles and predictive analytics. Although, AGI has great potential, multiple challenges, such as ethical issues, data privacy, and other technical challenges, must be addressed before its launch in the real world.
A Tree to Sequence Hybrid Model for Phishing Detection Pigilam Prathyushae, Potlacheruvu Rishi, Kola Prajna, Bharath Bhushan, Devulapalli Sudheer, Naga Malleswararao P 1st IEEE International Conference on Data Science and Intelligent Network Computing Icdsinc 2025, 2025 Now-a-days, phishing attacks are becoming more and more complicated, creating big trouble for normal detection systems. The growing smartness of these phishing attacks is showing the need for detection models which are more adaptive and can handle complex data relationships. In this paper, we are proposing a hybrid learning model that is mixing Gradient Boosted Decision Trees (XGBoost) together with Long ShortTerm Memory (LSTM) neural network for the detection of phishing websites. Unlike the usual models which are using ensemble learning and neural networks separately, our proposed system is using XGBoost to take out the decision-path features, and then these are converted into sequence form for giving to LSTM. In this way, the model can learn both nonlinear feature relations and the sequential dependencies inside the decision logic. We are testing our framework on a big phishing dataset and it is showing very strong performance with good generalization. The model achieved around 91% accuracy. By combining the explainability of tree models with the sequence learning ability of deep networks, this work is providing a scalable and modular approach which is improving on many drawbacks of existing systems and is giving one step forward for intelligent cybersecurity solutions.
Classification of vegetation, soil and water bodies of Telangana region using spectral indices Devulapalli Sudheer, S. Nagini, Naga Sreenija Meka, Yasaswini Kolli, Anudeep Eloori, Nithish Kumar Chowdam, Rushikesh Reddy Dorolla Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024 Scientists have developed vegetation indices for qualitative and quantitative evaluation of vegetative cover using spectral data in remote sensing applications. A complex blend of vegetation, soil brightness, environmental impacts, shadow, soil color, and moisture can be visible in the spectral response of vegetated areas. Additionally, the spatial-temporal fluctuations of the atmosphere impact the spectral indices. In the last 20 years, more than 40 indices have been made to improve categorization response and lessen the effects of the things listed above. Vegetation indices are numerical measurements that show how vigorous the vegetation is. They have greater sensitivity for the identification of biomass than individual spectral bands. These indices are interesting because they can be used to evaluate remote-sensing images. In particular, they help detect land use changes (temporal data), assess vegetative cover density, tell the difference between different crop types, and predict agricultural yields. Most of these indices are interested in improving classifications in the domain of thematic mapping. This project will list and describe most of the chosen regions’ green vegetation, soil types, and water bodies. The proposed method has compared how they have changed over time. It will also use spectral indices to sort these areas into groups.
Enhancing Connectivity in Rural Areas: Secure Spectrum Access in 6G Networks Using Advanced Encryption and Spectrum Sensing Techniques P Deepanramkumar, A Helen Sharmila, Niranchana Radhakrishnan, Devulapalli Sudheer, Jeethu V. Devasia, Ch. Pradeep Reddy, Gokul Yenduri, N. Jaisankar IT Professional, 2024 The advancement of 6G cognitive radio networks aims to reduce latency in rural and remote areas. Very few studies have been conducted on this technology. Therefore, this study utilizes massive multiple-input, multiple-output (MIMO) technology for secure data transmission at 6G base stations. Blockchain technology authenticates IDs and maintains secure records for network users, with decentralization achieved through the chimp optimization algorithm. The availability of the spectrum is monitored using the Q-learning hidden sparse variate logistic regression model, and the channel-state information is predicted using the quasi-Newton iterative unscented Kalman filter algorithm. Additionally, beamforming is enhanced through cooperative strategies. Secure routing is facilitated by the golden eagle optimization-hyper elliptic curve cryptography algorithm, where data are routed according to paths determined by the Dijkstra algorithm. The MIMO-6G-cognitive radio-based Internet-of-Things framework performs better compared to existing methods.
Predicting and Analyzing Air Quality Features Effectively using a Hybrid Machine Learning Model Nilankar Bhanja, Akila A, Devulapalli Sudheer, Ashok Kumar, Pramit Brata Chanda, Rakesh Dani Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing Icaaic 2023, 2023 The problem of atmospheric air pollution is one of the key environmental problems. In order to determine the factors that make the greatest contribution to air pollution and to counter them in a timely manner, it becomes necessary to constantly monitor the air environment. Currently, monitoring is carried out at stationary sources of pollutants, however, the share of pollution by exhaust gases of motor vehicles has increased. Thus, in order to obtain an objective picture, it is necessary to monitor pollution by motor vehicles, which, with the classical approach, using a variety of gas analyzers, is extremely costly. It is proposed to assess the state of the atmosphere indirectly, through calculations, based on the state of weather conditions, terrain, traffic intensity and car models, from which it is possible to obtain information on the type and amount of emitted pollutants. The article discusses the applicability of machine learning algorithms to the problem of predicting the state of air pollution. A review of the main prediction models was carried out, as well as the effectiveness of their application. Model prediction time estimates are obtained for a fixed error value.
Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagnosis of Pancreatic Tumor using Region-based Segmentation Techniques Nilankar Bhanja, Akila A, Devulapalli Sudheer, Ashok Kumar, PramitBrata Chanda, Rakesh Dani Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing Icaaic 2023, 2023 Globally, the pancreatic tumor is one of the principal sources of cancer death. This is because of a deficiency in promising tools for prompt identification of this cancer. Nowadays, the automatic discovery of pancreatic cancers with the help of novel computed tomography is extensively used for the analysis and presentation of pancreatic tumors. Conventional approaches are capable of extracting only low-level features. Tumors in pancreatic malignant that extremely impends the life span of infected people. Categorization of tumors without human intervention is a really challenging task. But image segmentation and classification have real-world complications, such as unbalanced categorization accuracy, a heavy workload, and the final outcomes determined by the subjective judgment of the medical expert during the analysis and presentation of pancreatic cancers. In addition, precise prediction of pancreatic cancers could help the clinical experts to provide the best therapeutic schedule for infected people of various stages. In this research work, Region-Based Segmentation (RBS) is used to segment the input images of pancreatic cancers. In case of feature extraction, Particle Swarm Optimization (PSO) _ Convolutional Neural Network (CNN), Cuckoo Algorithm _ Convolutional Neural Network (CNN), Modified Cuckoo Algorithm _ Convolutional Neural Network (CNN) are adopted. Results are evaluated based on Accuracy, Precision, Recall, time period. Results have proven that the proposed Modified Cuckoo Algorithm_ Convolutional Neural Network (CNN) performs better in all aspects.
Classification of vegetation, soil and water bodies of Telangana region using spectral indices Devulapalli Sudheer, S. Nagini, Naga Sreenija Meka, Yasaswini Kolli, Anudeep Eloori, Nithish Kumar Chowdam, Rushikesh Reddy Dorolla Artificial Intelligence Blockchain Computing and Security Volume 1, 2023 Scientists have developed vegetation indices for qualitative and quantitative evaluation of vegetative cover using spectral data in remote sensing applications. A complex blend of vegetation, soil brightness, environmental impacts, shadow, soil color, and moisture can be visible in the spectral response of vegetated areas. Additionally, the spatial-temporal fluctuations of the atmosphere impact the spectral indices. In the last 20 years, more than 40 indices have been made to improve categorization response and lessen the effects of the things listed above. Vegetation indices are numerical measurements that show how vigorous the vegetation is. They have greater sensitivity for the identification of biomass than individual spectral bands. These indices are interesting because they can be used to evaluate remote-sensing images. In particular, they help detect land use changes (temporal data), assess vegetative cover density, tell the difference between different crop types, and predict agricultural yields. Most of these indices are interested in improving classifications in the domain of thematic mapping. This project will list and describe most of the chosen regions’ green vegetation, soil types, and water bodies. The proposed method has compared how they have changed over time. It will also use spectral indices to sort these areas into groups.
Business analysis during the pandemic crisis using deep learning models Sudheer Devulapalli, Venkatesh B., Ramasubbareddy Somula AI Driven Intelligent Models for Business Excellence, 2022 This chapter aims to investigate pandemic crisis in the various business fields like real estate, restaurants, gold, and the stock market. The importance of deep learning models is to analyse the business data for future predictions to overcome the crisis. Most of the recent research articles are published on intelligent business models in sustainable development and predicting the growth rate after the pandemic crisis. This clear study will be presented based on all reputed journal articles and information from business magazines on the various business domains. Comparison of best intelligent models in business data analysis will be done to transform the business operations and the global economy. Different deep learning applications in business data analysis will be addressed. The deep learning models are investigated which are applied on descriptive, predictive, and prescriptive business analytics.
Study of Feature Extraction Techniques for BCI Processing Devulapalli Sudheer, Jothiaruna N, Anupama Potti, Gangappa M, Somula RamaSubbareddy 2022 International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2022, 2022
An efficient image retrieval system using edge, LBP and wavelet based texture analysis Journal of Advanced Research in Dynamical and Control Systems, 2018
A review of visual information retrieval on massive image data using hadoop International Journal of Control Theory and Applications, 2016
RECENT SCHOLAR PUBLICATIONS
Prediction of Alzheimer’s Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization A Bhansali, D Sudheer, S Tiwari, VS Desanamukula, F Ahmad Journal of Imaging Informatics in Medicine 38 (4), 2210-2228 , 2025 2025
Artificial general intelligence: Advancements, challenges, and future directions in AGI research G Yenduri, R Murugan, PKR Maddikunta, S Bhattacharya, D Sudheer, ... IEEE Access , 2025 2025 Citations: 32
Enhancing Connectivity in Rural Areas: Secure Spectrum Access in 6G Networks Using Advanced Encryption and Spectrum Sensing Techniques P Deepanramkumar, AH Sharmila, N Radhakrishnan, D Sudheer, ... IT Professional 26 (4), 22-28 , 2024 2024 Citations: 1
Sustainable Rural Livelihood through Backyard Poultry Farming D Sudheer, PK Pankaj, DBV Ramana, S Vijayakumar, G Srikrisha, ... JOURNAL OF KRISHI VIGYAN Учредители: Diva Enterprises Private Limited 12 (4 … , 2024 2024
Classification of vegetation, soil and water bodies of Telangana region using spectral indices D Sudheer, S Nagini, NS Meka, Y Kolli, A Eloori, NK Chowdam, ... Artificial Intelligence, Blockchain, Computing and Security Volume 1, 93-99 , 2023 2023
Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination SJ Sultanuddin, D Sudhee, P Prakash Satve, M Sumithra, ... Journal of Intelligent & Fuzzy Systems 45 (5), 8449-8463 , 2023 2023 Citations: 20
Predicting and Analyzing Air Quality Features Effectively using a Hybrid Machine Learning Model N Bhanja, A Akila, D Sudheer, A Kumar, PB Chanda, R Dani 2023 2nd International Conference on Applied Artificial Intelligence and … , 2023 2023 Citations: 4
Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagnosis of Pancreatic Tumor using Region-based Segmentation Techniques N Bhanja, A Akila, D Sudheer, A Kumar, PB Chanda, R Dani 2023 2nd International Conference on Applied Artificial Intelligence and … , 2023 2023 Citations: 3
Business analysis during the pandemic crisis using deep learning models S Devulapalli, B Venkatesh, R Somula AI-driven intelligent models for business excellence, 68-80 , 2023 2023 Citations: 6
Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques S Devulapalli, A Potti, R Krishnan, MS Khan Materials Today: Proceedings 81, 983-988 , 2023 2023 Citations: 34
Study of Feature Extraction Techniques for BCI Processing D Sudheer, N Jothiaruna, A Potti, M Gangappa, S RamaSubbareddy 2022 International Conference on Smart Generation Computing, Communication … , 2022 2022
Iceberg detection and tracking using two-level feature extraction methodology on Antarctica Ocean R Krishnan, A Thangavelu, P Panneer, S Devulapalli, A Misra, D Putrevu Acta Geophysica 70 (6), 2953-2963 , 2022 2022 Citations: 6
STUDENT PERFORMANCE ANALYSIS FOR OUTCOME BASED EDUCATION. PS RAO, S NAGINI, D Sudheer, VSS BAPIRAJ, MV VARDHAN, ... International Journal of Early Childhood Special Education 14 (5) , 2022 2022 Citations: 2
The Study of DDOS Attacks and Classification Performance Using Machine Learning Techniques DSMKAPGMD Manasa European Journal of Molecular & Clinical Medicine 9 (8), 966-978 , 2022 2022
Adaptive local neighborhood range based firefly algorithm for link prediction P Srilatha, S Ramasubbareddy, D Sudheer International Journal of System Assurance Engineering and Management, 1-15 , 2021 2021 Citations: 1
Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features R Krishnan, A Thangavelu, P Prabhavathy, D Sudheer, D Putrevu, ... International Journal of Intelligent Computing and Cybernetics 14 (4), 533-549 , 2021 2021 Citations: 5
Study of Predicting Heart Diseases Using KNN, Decision Tree and Random Forest Methods S Devulapalli, A Potti, NA Devi, CC Reddy IJCSE 9 (8), 27-30 , 2021 2021
Remote sensing image retrieval by integrating automated deep feature extraction and handcrafted features using curvelet transform S Devulapalli, R Krishnan Journal of Applied Remote Sensing 15 (1), 016504-016504 , 2021 2021 Citations: 27
Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system RK Sudheer Devulapalli J. Appl. Remote Sens 13 (3), 034519 , 2019 2019 Citations: 20
Multiscale Texture Analysis and Color Coherence Vector Based Fea-ture Descriptor for Multispectral Image Retrieval D Sudheer, R Krishnan ASTES 4 (6), 270-279 , 2019 2019 Citations: 11
MOST CITED SCHOLAR PUBLICATIONS
Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques S Devulapalli, A Potti, R Krishnan, MS Khan Materials Today: Proceedings 81, 983-988 , 2023 2023 Citations: 34
Artificial general intelligence: Advancements, challenges, and future directions in AGI research G Yenduri, R Murugan, PKR Maddikunta, S Bhattacharya, D Sudheer, ... IEEE Access , 2025 2025 Citations: 32
Remote sensing image retrieval by integrating automated deep feature extraction and handcrafted features using curvelet transform S Devulapalli, R Krishnan Journal of Applied Remote Sensing 15 (1), 016504-016504 , 2021 2021 Citations: 27
Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination SJ Sultanuddin, D Sudhee, P Prakash Satve, M Sumithra, ... Journal of Intelligent & Fuzzy Systems 45 (5), 8449-8463 , 2023 2023 Citations: 20
Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system RK Sudheer Devulapalli J. Appl. Remote Sens 13 (3), 034519 , 2019 2019 Citations: 20
Multiscale Texture Analysis and Color Coherence Vector Based Fea-ture Descriptor for Multispectral Image Retrieval D Sudheer, R Krishnan ASTES 4 (6), 270-279 , 2019 2019 Citations: 11
Edge and Texture Feature Extraction Using Canny and Haralick Textures on SPARK Cluster RSPB D.Sudheer 2nd International Conference on Data Engineering and Communication … , 2017 2017 Citations: 7
A REVIEW OF VISUAL INFORMATION RETRIEVAL ON MASSIVE IMAGE DATA USING HADOOP DS K. Rajakumar International Journal of control theory and applications 9 (28), 6 , 2016 2016 Citations: 7
Business analysis during the pandemic crisis using deep learning models S Devulapalli, B Venkatesh, R Somula AI-driven intelligent models for business excellence, 68-80 , 2023 2023 Citations: 6
Iceberg detection and tracking using two-level feature extraction methodology on Antarctica Ocean R Krishnan, A Thangavelu, P Panneer, S Devulapalli, A Misra, D Putrevu Acta Geophysica 70 (6), 2953-2963 , 2022 2022 Citations: 6
performance evolution of hadoop distributed file system ARL D. Sudheer international journal of computer science and engineering 3 (9), 6 , 2015 2015 Citations: 6
Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features R Krishnan, A Thangavelu, P Prabhavathy, D Sudheer, D Putrevu, ... International Journal of Intelligent Computing and Cybernetics 14 (4), 533-549 , 2021 2021 Citations: 5
Predicting and Analyzing Air Quality Features Effectively using a Hybrid Machine Learning Model N Bhanja, A Akila, D Sudheer, A Kumar, PB Chanda, R Dani 2023 2nd International Conference on Applied Artificial Intelligence and … , 2023 2023 Citations: 4
Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagnosis of Pancreatic Tumor using Region-based Segmentation Techniques N Bhanja, A Akila, D Sudheer, A Kumar, PB Chanda, R Dani 2023 2nd International Conference on Applied Artificial Intelligence and … , 2023 2023 Citations: 3
An Efficient Image Retrieval System Using Edge, LBP and Wavelet based Texture Analysis KR D. Sudheer Journal of Advanced Research in Dynamical and Control Systems 10 (10), (1629 … , 2018 2018 Citations: 3
STUDENT PERFORMANCE ANALYSIS FOR OUTCOME BASED EDUCATION. PS RAO, S NAGINI, D Sudheer, VSS BAPIRAJ, MV VARDHAN, ... International Journal of Early Childhood Special Education 14 (5) , 2022 2022 Citations: 2
Enhancing Connectivity in Rural Areas: Secure Spectrum Access in 6G Networks Using Advanced Encryption and Spectrum Sensing Techniques P Deepanramkumar, AH Sharmila, N Radhakrishnan, D Sudheer, ... IT Professional 26 (4), 22-28 , 2024 2024 Citations: 1
Adaptive local neighborhood range based firefly algorithm for link prediction P Srilatha, S Ramasubbareddy, D Sudheer International Journal of System Assurance Engineering and Management, 1-15 , 2021 2021 Citations: 1
Prediction of Alzheimer’s Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization A Bhansali, D Sudheer, S Tiwari, VS Desanamukula, F Ahmad Journal of Imaging Informatics in Medicine 38 (4), 2210-2228 , 2025 2025
Sustainable Rural Livelihood through Backyard Poultry Farming D Sudheer, PK Pankaj, DBV Ramana, S Vijayakumar, G Srikrisha, ... JOURNAL OF KRISHI VIGYAN Учредители: Diva Enterprises Private Limited 12 (4 … , 2024 2024
