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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Syamala, Likith Sai Reddy Kavuluri, V.N.K.D. Sai Chandana Dasa, Sai Sneha Lakshman, D.S. Kumar, Munish Kumar, Sandeep Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350350067

The term 'Cryptography', derived from 'crypt' meaning hidden and 'graphy' meaning writing, is vital for data encryption, ensuring confidentiality, integrity, and availability (CIA). Encryption transforms plain text into cipher text, securing data at rest and in transit. Confidentiality is upheld by encryption, rendering data unreadable without the decryption key. Encryption also maintains integrity by preventing unauthorized data alterations. It operates at the sender's end, with decryption at the receivers. This paper focuses on password security and modern encryption algorithms for message protection. Securing sensitive data in digital communication relies heavily on password practices. Symmetric key algorithms like AES and DES, along with asymmetric ones such as RSA and Elliptic Curve Cryptography (ECC) are explored. A thorough analysis of their strengths and weaknesses is conducted, considering their applicability in contemporary digital security contexts. © 2024 IEEE.

关键词： Ciphertext

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CNN Design with AlexNet Algorithm for Diagnosis of Diseases in Cassava Leaves 4

CNN Design with AlexNet Algorithm for Diagnosis of Diseases ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Roshini, Polagani Khajavali, Shaik Snigdha, M L Sneha Harsha, Badugu Deva Srilakshmi, Bandlamudi Gopi, Arepalli Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350385793

Plant diseases play a significant role in reducing crop productivity. Nobody will be able to consume the crops because they have been contaminated with different illnesses. As a result, farmers may face huge losses. Cassava is a staple crop in some nations, so this issue may have severe impact on the economy. Detecting plant diseases manually takes time and is prone to errors. It is not always a reliable method for discovering and avoiding the propagation of plant diseases. In order to overcome these issues, cutting-edge technology like machine learning and deep learning may help with early plant disease identification. The main goal of the work is to use deep learning to image classification in order to accurately identify diseases that especially impact cassava plants. This identification may make it possible to implement early intervention measures like specific application of chemicals or confinement of contaminated crops. Each and every test and training image comes from a rural area in the natural world. Using a specific collection of data, the model has been verified to ascertain its true results. In a nutshell this work uses deep learning methods to combat cassava diseases, supporting agricultural practices and food security. The development of a precise disease detection and prevention model has the potential to significantly increase the capacity for recovery of the cassava crop, improving food production and the quality of life for those whose livelihoods depend on this paramount crop. © 2024 IEEE.

关键词： AlexNet Cassava Deep Learning Disease Identification Intervention

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Spoken Keyword Detection in Speech Processing using Error Rate Estimations 15

Spoken Keyword Detection in Speech Processing using Error Ra...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Manish, Katakam Venkata Naga Sai Prabhu, K. Arjun, Koppineni Harish Kanth, Surya Aravinth, S.S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

ISBN: (纸本)9798331300579

Spoken keyword detection is a technique used to identify specific keywords or phrases in spoken language. It is often used in the field of speech recognition to trigger specific actions or responses. For example, a spoken keyword detection system might be used to activate a voice-controlled assistant or to initiate a search query. Keyword detection systems can be trained to recognize a wide range of keywords, depending on the specific needs of the application. They may use machine learning techniques to analyze the audio signal and identify patterns that correspond to specific keywords. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Cloud-Powered Mobility: A Seamless Integration Approach for Mobile Apps 8

Cloud-Powered Mobility: A Seamless Integration Approach for ...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Satwik, P.V.S. Reddy, T. Sai Jeevan Prabhas, B. Sai Burugari, Vijay Kumar Sridhar, P.S.V.S. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350386578

As cloud services continue to gain importance, their integration with mobile applications lags, despite notable exceptions such as Telegram. This research study analyzes the foundational aspects of cloud computing systems, their organization, architecture, and the need for mobile application integration. It explains the necessary development tools and motivations driving app migration to the cloud, while also exploring the fundamentals and layers of mobile cloud computing systems. Through an analysis of techniques like data synchronization and computational offloading, this study highlights the importance of security and reliability. Case studies across domains demonstrate the scalability of this framework for next-generation mobile applications. Furthermore, security and privacy concerns are addressed, alongside an exploration of the services offered by mobile-based cloud providers. Highlighting measures for ensuring security, privacy, and reliability in cloud-powered mobility solutions, this research study covers fault tolerance, data encryption, and authentication systems. Integration with major cloud providers like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform is discussed, along with advanced methodologies and recommendations for future advancements in the field. © 2024 IEEE.

关键词： Cloud platforms

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Abnormal Activity Detection in Crowded Areas 7

Abnormal Activity Detection in Crowded Areas

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Tummalapalli, Sohan Pillagolla, Durga Jayasai Karpurapu, Aparna Vaishnavi Potla, Narayana Swami Joseph, Iwin Thanakumar Koneru Lakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350359299

Detecting abnormal activity in crowded areas poses a significant challenge due to the intricate nature of crowd dynamics and the difficulty in defining what constitutes normal behaviour. It is an effort to maintain security and prevent crime. In this paper we examine the advancements in identifying abnormal activity within crowded areas. Our focus lies on techniques that utilize computer vision and machine learning to detect abnormal behaviour in videos and images of populated scenes. We also discuss the challenges in identifying abnormal activity in certain locations and discuss possible directions for further investigation. © 2024 IEEE.

关键词： computer vision

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Machine Learning and Deep Learning Approaches for Healthcare Predictive Analytics 5

Machine Learning and Deep Learning Approaches for Healthcare...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Charan, Bakka Jaswanth, Dasu Hemanth, E. Naidu, Mathireddy Sumanth Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur AP Vaddeswaram India

ISBN: (纸本)9798350379945

Healthcare prediction has been essential to life-saving measures in recent years. Intelligent technologies that evaluate complex data linkages and convert them into actionable insights for prediction purposes are rapidly emerging in the healthcare industry. Artificial intelligence (AI) is the main force behind this revolutionary process, with a focus on deep learning and machine learning systems. By utilizing a variety of data sources, including clinical data and medical imaging, these technologies are essential for both detecting and forecasting illnesses. When AI -driven predictive analytics are used in healthcare, they may simulate human perception and, in certain situations, even identify illnesses that may be invisible to the human eye. This results in significant therapeutic support. One cannot overestimate the importance of predictive analytics in healthcare as it directly affects how accurately diseases are predicted. While incorrect forecasts might put patients in danger, precise and timely forecasts have the power to save lives. Thus, the creation of trustworthy and effective techniques for predictive analysis in healthcare is essential. With an emphasis on healthcare prediction, this article aims to provide an extensive overview of state-of-the-art machine learning and deep learning techniques. Furthermore, the goal is to recognize and tackle the innate difficulties linked to implementing these strategies in the healthcare industry. © 2024 IEEE.

关键词： Adversarial machine learning

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Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 502-510页

作者： Venkateswarlu Gundu Sishaj P.Simon Vemalaiah Kasi Narayana Prasad Padhy Dheeraj Kumar Khatod Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation GunturGunturIndia Department of Electrical and Electronics Engineering National Institute of Technology TiruchirappalliTiruchirappalliIndia Department of Electrical Engineering(EED) Indian Institute of Technology Roorkee(IITR)RoorkeeIndia

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** crowding among residential customers is one of such *** scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed *** order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy *** the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority *** non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding *** the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global *** proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

关键词： Crowding strategy demand response distribution system General Algebraic Modeling System(GAMS) local energy community second-order cone programming

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Predictive Modeling of Gold Prices Using Machine Learning

Predictive Modeling of Gold Prices Using Machine Learning

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Sri Nithya, N Ch Pamidi, Preethi Tumula, Poojitha Voora, Hema Harshitha Balajee, R.M. Srithar, S. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350362879

In this pioneering study, this paper harnesses the capabilities of state-of-The-Art machine learning methodologies to address the formidable challenge of forecasting gold prices within the tumultuous terrain of the financial market. Our innovative technique relies on a vast and comprehensive dataset, encompassing an extensive array of economic indicators and the intricate movements of foreign currencies. By deploying a RandomForestRegressor model, the findings establish a robust framework for generating reliable and precise forecasts, offering a strategic advantage in navigating the complexities of the volatile gold *** the quantitative realm, our research delves into the qualitative domain by undertaking an in-depth exploration of textual data. Through a meticulous examination of text, the findings unveil latent patterns and interconnections, employing a diverse set of visual tools to enhance interpretability. AutoViz, a powerful ally in our analysis, facilitates a comprehensive visual examination of crucial attributes, augmenting our understanding of the factors influencing gold *** from exploratory analysis to predictive modeling, we rigorously evaluate the effectiveness of our approach through quantitative measures, including the widely recognized R-squared error. The application of RandomForestRegressor to glean insights from historical data becomes integral in refining our gold price predictions. Visual representations derived from our expansive dataset not only validate the model's proficiency in anticipating gold prices with precision but also serve as compelling evidence of the efficacy of machine learning in financial *** groundbreaking approach holds transformative potential, offering a tangible enhancement to financial forecasts. Its practical implications extend to empowering a diverse spectrum of stakeholders, including investors, researchers, and legislators, in managing the intricate dynamics of the financial s

关键词： Forecasting

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EmoStream: Real-time Emotion Prediction through Speech with Apache Flink and Kafka Integration 15

EmoStream: Real-time Emotion Prediction through Speech with ...

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

作者： Karri, Naga Bhuvana Ponamala, Ishitha Gudla, Vijayatejaswari Vemula, Swarna Raju Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350370249

Advancements in real-time emotion recognition systems have witnessed a surge in interest, fuelled by their potential applications in diverse fields such as human computer interaction, mental health monitoring, and user experience enhancement. Existing approaches often focus on singular modalities, such as speech analysis or facial expressions. However, a comprehensive understanding of human emotion necessitates the integration of multiple modalities. This research introduces a novel approach, a Multimodal Real-time Emotion Prediction System (MREPS), which combines speech analysis with streaming data processing. The system begins by capturing a continuous stream of audio data, which undergoes Speech-to-Text (STT) conversion using cloud-based services like Google Cloud Speech-to-Text. Simultaneously, facial expression recognition and physiological signal analysis can be integrated for a more comprehensive emotional profile. The real-time nature of the data necessitates the use of a robust streaming architecture, leveraging technologies such as Apache Kafka for data ingestion and Apache Flink for stream processing. A crucial component of the system is the Emotion Prediction API, a microservice housing a machine learning model for emotion classification. This model is trained on diverse datasets to ensure accuracy across various languages, cultures, and individual differences. The real-time API endpoint continuously processes the multimodal input, providing dynamic emotion predictions. © 2024 IEEE.

关键词： Apache Flink Apache Kafka Cloud-based services Docker Emotion Prediction API Facial expression recognition Human-computer interaction Kubernetes Machine learning model Mental health monitoring Microservices Multimodal data integration Multimodal emotion prediction system Physiological signal analysis Privacy Real-time emotion recognition Security measures Speech analysis Speech-to Text Streaming architecture User feedback

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Interpreting Human Feelings: A Comparison of Speech-Based Identification Methods 10

Interpreting Human Feelings: A Comparison of Speech-Based Id...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Manoj, N. Sai, T. Rahul Mahitha, N. Lakshmi Tanmayi, K. Ashesh, K. Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350384369

Emotion recognition from speech is the method of identifying and classifying the emotions of a speaker which delivers through their voice. It is a challenging and important application of voice processing and computational paralinguistics. We performed a comparative study comparing four popular techniques scalogram, Fourier parameters, incomplete sparse least square regression to determine which one is the most accurate for speech-based emotion detection. The usefulness of a few speech-based emotion recognition methods is clarified in this paper, along with their potential applications in other domains. There are numerous benefits to using emotion recognition from speech, such as improved mental health, tailored information, and improved customer service. Emotion identification from speech is easy to integrate since it is a feature of many modern systems and technologies. This study explains the utility of several speech-based emotion recognition techniques and discusses their possible uses in other fields. Speech recognition for emotion identification has many advantages, including better mental health, personalized information, and enhanced customer support. Since many contemporary systems and technologies already have emotion detection built in, integrating emotion identification from speech is simple. © 2024 IEEE.

关键词： Emotion Recognition

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