S. Chandrakala

27 papers B 2Journal 22Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Denoising Convolutional Autoencoder Based Approach for Disordered Speech Recognition. Int. J. Artif. Intell. Tools S. Chandrakala, Vishnika Veni S
2024	J	`jnl`	Spectro Temporal Fusion with CLSTM-Autoencoder based approach for Anomalous Sound Detection. Neural Process. Lett. S. Chandrakala, Akhilandeswari Pidikiti, P. V. N. Sai Mahathi
2023	J	`jnl`	Anomalous human activity detection in videos using Bag-of-Adapted-Models-based representation. Pattern Anal. Appl. S. Chandrakala
2023	J	`jnl`	Anomaly detection in surveillance videos: a thematic taxonomy of deep models, review and performance analysis. Artif. Intell. Rev. S. Chandrakala, K. Deepak, G. Revathy
2022	J	`jnl`	Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos. Expert Syst. Appl. S. Chandrakala, K. Deepak, L. K. P. Vignesh
2022	J	`jnl`	Intelligibility assessment of impaired speech using Regularized self-representation based compact supervectors. Comput. Speech Lang. S. Malini, S. Chandrakala
2022	J	`jnl`	Object-centric and memory-guided network-based normality modeling for video anomaly detection. Signal Image Video Process. S. Chandrakala, P. Shalmiya, V. Srinivas, K. Deepak
2022	J	`jnl`	V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos. IEEE Trans. Comput. Soc. Syst. S. Chandrakala, L. K. P. Vignesh
2021	—	`conf`	Analysis of Global Word Representations for Depression Detection. ISIC Niveditha Sekar, S. Chandrakala, G. Prakash
2021	J	`jnl`	Bag of Models Based Embeddings for Assessment of Neurological Disorders Using Speech Intelligibility. IEEE Trans. Emerg. Top. Comput. S. Chandrakala, S. Malini, S. L. Jayalakshmi
2021	J	`jnl`	Deep Multi-view Representation Learning for Video Anomaly Detection Using Spatiotemporal Autoencoders. Circuits Syst. Signal Process. K. Deepak, G. Srivathsan, S. Roshan, S. Chandrakala
2021	J	`jnl`	Investigation of DNN-HMM and Lattice Free Maximum Mutual Information Approaches for Impaired Speech Recognition. IEEE Access Vishnika Veni S, S. Chandrakala
2021	J	`jnl`	Multi-view representation for sound event recognition. Signal Image Video Process. S. Chandrakala, Venkatraman M, Shreyas N, S. L. Jayalakshmi
2021	J	`jnl`	Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies. Neural Process. Lett. S. Chandrakala, V. Srinivas, K. Deepak
2021	J	`jnl`	Residual spatiotemporal autoencoder for unsupervised video anomaly detection. Signal Image Video Process. K. Deepak, S. Chandrakala, C. Krishna Mohan
2020	J	`jnl`	A Similarity Based Representation for Identifying Healthcare Anomalous Activities. J. Medical Imaging Health Informatics K. Deepak, Mohamed Yacin Sikkandar, S. Siddharth, S. Chandrakala
2020	J	`jnl`	Autocorrelation of gradients based violence detection in surveillance videos. ICT Express K. Deepak, L. K. P. Vignesh, S. Chandrakala
2020	J	`jnl`	Generative Model Driven Representation Learning in a Hybrid Framework for Environmental Audio Scene and Sound Event Recognition. IEEE Trans. Multim. S. Chandrakala, S. L. Jayalakshmi
2019	J	`jnl`	Environmental Audio Scene and Sound Event Recognition for Autonomous Surveillance: A Survey and Comparative Studies. ACM Comput. Surv. S. Chandrakala, S. L. Jayalakshmi
2017	J	`jnl`	Erratum to: MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks. Wirel. Pers. Commun. A. Pravin Renold, S. Chandrakala
2017	J	`jnl`	MRL-SCSO: Multi-agent Reinforcement Learning-Based Self-Configuration and Self-Optimization Protocol for Unattended Wireless Sensor Networks. Wirel. Pers. Commun. A. Pravin Renold, S. Chandrakala
2016	J	`jnl`	Survey on state scheduling-based topology control in unattended wireless sensor networks. Comput. Electr. Eng. A. Pravin Renold, S. Chandrakala
2010	J	`jnl`	Classification of varying length multivariate time series using Gaussian mixture models and support vector machines. Int. J. Data Min. Model. Manag. S. Chandrakala, C. Chandra Sekhar
2009	—	`conf`	Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features. PReMI S. Chandrakala, C. Chandra Sekhar
2009	—	`conf`	Model Based Clustering of Audio Clips Using Gaussian Mixture Models. ICAPR S. Chandrakala, C. Chandra Sekhar
2008	B	`conf`	A density based method for multivariate time series clustering in kernel feature space. IJCNN S. Chandrakala, C. Chandra Sekhar
2007	B	`conf`	Local Density Estimation based Clustering. IJCNN Sheetal Reddy Pamudurthy, S. Chandrakala, C. Chandra Sekhar

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.