The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken...
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Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredic...
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In this study, we propose a methodology for classifying electrocardiogram (ECG) signals into normal and myocardial infarction (MI) classes. The methodology consists of three main steps: pre-processing, segmentation, a...
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The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slic...
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Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be u...
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Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workpl...
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Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security, workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such as voice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, a historical and generally established approach, may be enhanced or replaced by these revolutionary real-time techniques. Traditional lie detection procedures, such as polygraph testing, have been challenged for their lack of reliability and validity. Newer techniques, such as brain imaging and machine learning, might offer better outcomes, although they are still in their early phases and require additional testing. This project intends to explore a deception-detection module based on sophisticated speech-stress analysis techniques that might be applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns, to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy of the system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems, by addressing the limitations of old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy, which uses random forest (RF) to select only the significant features for training when a real-life trial dataset consisting of audio files is employed. Next, utilizing the RF as a classifier, an accuracy of 88% is reached through comprehensive evaluation, thereby confirming its reliability and precision for lie-detection in real-time scena
Very recent attacks like ladder leak demonstrated feasibility to recover private key with side channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA ...
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Surveillance through video surveillance is the basis for the increasing demand for security. Users who are capable can manipulate video images, timestamps, and camera settings digitally;they can also physically manipu...
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The Internet of Things (IoT) encompasses all Internet communication technologies. In particular, wireless sensor networks (WSNs) play an important role in various IoT applications, such as home network, smart factory,...
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The Internet of Things (IoT) encompasses all Internet communication technologies. In particular, wireless sensor networks (WSNs) play an important role in various IoT applications, such as home network, smart factory, and smart city. The Internet engineering Task Force (IETF), an internet standardization organization, had proposed a lightweight protocol called constrained application protocol (CoAP) for the Internet connectivity of low-performance devices such as WSNs. Because the CoAP employed the user datagram protocol, and a simple congestion control mechanism based on binary exponential backoff, it showed significant delay in lossy network conditions. To overcome this, the IETF Constrained RESTful Environments (CoRE) working group proposed the CoAP Simple Congestion Control/Advanced (CoCoA) algorithm. However, the CoCoA algorithm suffered from high computational overhead for RTO calculation at every transmission of packets, leading to increased energy consumption by the sensor nodes. Moreover, the use of a fixed weighting parameter in the calculation of round-trip time (RTT) resulted in a slow response to the rapidly changing network environment. This study proposes an algorithm to efficiently assess the network conditions by measuring the RTT and the number of re-transmissions over a certain period or number of communication rounds. Statistical techniques were applied to determine the network’s loss rate;further, based on the identified loss rate, different weighting factors (α) were applied to calculate the predicted RTT values. Proposed algorithm was designed to reduce the computational overhead for RTO calculations and to be adaptive to the network conditions exhibiting significant RTT variations. The algorithm was compared with CoCoA and the existing smoothed round trip time (SRTT) algorithm applied in the traditional Internet using the Cooja simulator. The simulations were performed under wireless environments with loss rates of 5%, 10%, and 15%, respectiv
With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advan...
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