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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zubair Zaland Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Muhammad Waseem Muhammad Qaseem Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept.of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual’s opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one’s own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-time data in order to forecast Pakistan’s next election results. Users can quickly share real-time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the “Tweepy’’ Python package and retrieved real-time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python’s “TextBlob’’ package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate.

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FPGA-based Low Power Acceleration of HARK Sound Source Localization

FPGA-based Low Power Acceleration of HARK Sound Source Local...

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IEEE Cool Chips

作者： Zirui Lin Katsutoshi Itoyama Kazuhiro Nakadai Hideharu Amano Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.

关键词： Location awareness Program processors Power demand Parallel processing Energy efficiency Matrix decomposition Robots

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DNS Tunnel Problem In Cybersecurity

DNS Tunnel Problem In Cybersecurity

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Güneş Gürsoy Asaf Varol Ahad Nasab Dept. of Computer Engineering College of Eng. and Natural Sciences Maltepe University İstanbul Turkey Dept. of Eng. Management and Technology College of Eng. and Computer Science The Uni. of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Name System (DNS) is the most important building block of the Internet. Websites, file transfer applications, and e-mail services use the DNS service. Therefore, these services can rarely be blocked by firewalls to prevent their access from being affected. Since applications such as firewalls and Intrusion Detection Systems (IDS) do not check the allowed protocols, attackers can open a secret path called DNS Tunnel through the DNS protocol to access sensitive data and cause many attacks. In this study, DNS Tunnel, DNS Tunnel detection methods and detection methods, and DNS attack types are included.

关键词： Deep learning Protocols Machine learning algorithms Firewalls (computing) Tunneling Malware Behavioral sciences

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Analysis of Elliptic Curve Cryptography & RSA

Journal of ICT Standardization

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Journal of ICT Standardization 2023年第4期11卷 355-378页

作者： Khan, Mohammad Rafeek Upreti, Kamal Alam, Mohammad Imran Khan, Haneef Siddiqui, Shams Tabrez Haque, Mustafizul Parashar, Jyoti Department of Computer & Network Engineering CS & IT College Jazan University 45142 Saudi Arabia Delhi NCR Ghaziabad India Department of Computer Science CS & IT College Jazan University 45142 Saudi Arabia Dr. D.Y. Patil Vidyapeeth’s Centre for Online Learning Dr. D.Y. Patil Vidyapeeth Pune [Deemed to be University India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management GGSIPU New Delhi India

In today’s digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman). © 2023 River Publishers.

关键词： Public key cryptography

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A Probabilistic Topology Inference Method for Networked Dynamical System via Single Excitation

A Probabilistic Topology Inference Method for Networked Dyna...

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IEEE Conference on Decision and control

作者： Qing Jiao Yushan Li Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method.

关键词： Lower bound Accuracy Network topology Noise Probabilistic logic Numerical simulation Topology Noise measurement Reliability Dynamical systems

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Enhancing Ophthalmic Diagnostics: CNNs in Cataract Detection

Enhancing Ophthalmic Diagnostics: CNNs in Cataract Detection

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Disruptive Technologies (ICDT), International Conference on

作者： Kartik Solanki Shaurya Pratap Singh Aman Yadav Vikrant Sharma Shashank Awasthi Satvik Vats Dept. of Computer Science Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science and Engineering G.L. Bajaj Institute of Management Greater Noida

Cataracts have been one of the most prevalent eye disorders, which often can cause significant visual impairment due to the clouding of the eye's lens. This condition can worsen in future, often leading to severe vision problems and even blindness. Consequently, detecting cataracts is paramount in mitigating the associated risks and preventing the onset of blindness. Throughout the years there has been quite good progress in leveraging cutting-edge technology, especially due to machine learning, to improve the perfection and efficacy of cataract detection. Convolutional Neural Networks (CNNs) have made an appearance as a commanding implement for computerized the systematization of eye images in the context of cataract identification. Proposed research was more concerned with fine-tune the process of cataract identification, aiming to increase the accuracy while minimizing the data loss. To accomplish this, we conducted a series of experiments, with a key focus on manipulating a critical parameter: the number of training epochs. The proposed research revealed a compelling relationship between the number of training epochs and the accuracy and loss of data in CNN. As we delved into a spectrum of epoch values, a clear pattern emerged: the higher the number of epochs, the more refined and potent the model became. In this research, a significant milestone was reached when utilizing a generous number of 90 epochs, resulting in an impressive accuracy rate of 97.37%.

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Linear Quadratic Regulator-based controller for control Mode Switching Under Fault Operation of Grid-Connected PV with Supercapacitor System

Linear Quadratic Regulator-based Controller for Control Mode...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Paychuda Kritprajun Leon M. Tolbert Nattapat Praisuwanna Jingxin Wang Yunting Liu Maximiliano Ferrari Dept. of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Dept. of Electrical Engineering The Pennsylvania State University PA USA Grid Components and Control Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable (completely charged) during fault operations. The SC improves the low-voltage ride-through (LVRT) capability of the PV system by quickly balancing power between the PV system and the grid. When the SC reaches its full charge capacity, the DC-link voltage will be controlled by the inverter’s controller, and the PV system may need to curtail its power generation. During the transition, fluctuations in the DClink voltage can occur which can compromise the stability of the PV system, especially when the control mode switching happens during severe fault operations. The proposed LQR-based controller is validated on a hardware testbed platform to demonstrate its effectiveness in reducing DC-link overvoltage during the control mode transitions. As a result, the PV system can remain compliant with LVRT requirements while maintaining stability during fault operations.

关键词： Voltage fluctuations Regulators Power quality Power control Switches Power system stability Supercapacitors control systems Voltage control Transient analysis

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Investigation of DQN Algorithm for Driving control for Smart Cities and Traffic Safety 4

Investigation of DQN Algorithm for Driving Control for Smart...

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4th International Informatics and Software engineering Conference, IISEC 2023

作者： Yigit, Yildiray Karabatak, Murat Varol, Asaf Nasab, Ahad Bitlis Eren University Vocational School of Ahlat Bitlis Turkey Firat University College of Technology Elaziǧ Turkey The University of Tennessee at Chattanooga Dept. of Engineering Management and Technology ChattanoogaTN United States The University of Tennessee at Chattanooga College of Engineering and Computer Science ChattanoogaTN United States

ISBN: (纸本)9798350318036

With the rapid increase in the world population day by day, the migration of people from rural areas to cities is also increasing rapidly. This rapid population growth in cities brings with it many problems such as transportation, accommodation and education that need to be solved. Traffic and transportation problems in cities are at the forefront of these problems. Due to the increasing traffic day by day, the time people spend in traffic is increasing, and depending on the traffic density, there can be great variability in the acceleration and deceleration of the drivers. In addition, studies have shown that accidents due to excessive acceleration and deceleration are more common at times when traffic density is low. The fact that vehicle drivers can drive comfortably and safely in traffic can bring many advantages. Many parameters, from driving safety to fuel consumption, from environmental pollution to comfort, can be provided by driving control. The main goal of this work is to create an action strategy that maximizes the overall reward in the long run. In this study, an optimum speed has been tried to be suggested for vehicles in urban traffic by using deep reinforcement learning. © 2023 IEEE.

关键词： Smart city

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Deep Learning Technique for Real-Time Forest Fire Detection ...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Nidhi Gupta Sujata Chakravarty Satyabrata Dash Dept. of Computer Science & Engineering Centurion University of Technology & Management Odisha India Dep. of Computer Science and Engineering Gandhi Institute of Technology & Management (GITAM Deemed to be University) Andhra Pradesh India

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.

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A Study on Gynecological Cancers Using Artificial Intelligence: A Revolutionary Approach

A Study on Gynecological Cancers Using Artificial Intelligen...

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2025 International Conference on Intelligent control, Computing and Communications, IC3 2025

作者： Upreti, Kamal Hunekari, Sheela Upreti, Shitiz Rai, Ashish Kumar Alam, Mohammad Shabbir Nasir, Mohammad Shahnawaz Christ University Dept. of Computer Science Delhi- NCR Campus Ghaziabad India School of Engineering and Technology Pimpri Chinchwad University Maharashtra India Department of Management Mullana Haryana Ambala India Ramchandra Chandravansi University Faculty of Commerce & Management Jharkhand India Cmj University Dept. of Computer Science India

ISBN: (纸本)9798331527495

An examination of the role of artificial intelligence (AI) in ovarian, cervical, and breast cancer early detection and management is presented in this paper. Artificial intelligence (AI) can improve diagnostic accuracy, streamline treatment protocols, and facilitate personalized medicine approaches by leveraging advancements in machine learning (ML) and deep learning (DL). Various Artificial Intelligence models have demonstrated success in improving the outcomes of cancer diagnostics, including their ability to distinguish benign from malignant tumors. Technology challenges and ethical issues related to the integration of AI into clinical practice are also discussed in the review. Specifically, we want to illustrate how artificial intelligence can lead to better prognoses and reduced mortality rates for cancer patients by enhancing early detection capabilities. © 2025 IEEE.

关键词： Deep learning

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