Managing medical devices efficiently was a challenge, especially with manual methods proving error-prone amid staff shortages. To overcome these issues, we developed a novel IoT powerstrip device for tracking usage an...
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Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short low-powered links between the devices. These D2D links can be used to fetch cached c...
Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short low-powered links between the devices. These D2D links can be used to fetch cached contents and therefore improve the application experiences, especially for multimedia contents. In this article, we investigate the effect of caching policies on the offloading probability (also known as the hit-rate), and hence the buffering time, for video streaming applications. Designing a caching policy to maximize the hit-rate depends on several factors such as device storage capacity, instantaneous number of devices available to act as a D2D transmitter, resource allocation policy, etc. We first model the D2D links as a simple birth-death process to realize the dynamicity of the D2D links, and derive the steady-state distribution of the number of D2D links. We show that the hit-rate can be improved by increasing the caching redundancy (number of copies of a given content to be cached). Finally, through simulation we show that there exists an optimal group size and redundancy, beyond which the hit-rate do not improve.
Blockchain has shown tremendous growth in the past few years because of its decentralized and immutable architecture, which makes transactions transparent, making it a feasible choice for authentication and security p...
Blockchain has shown tremendous growth in the past few years because of its decentralized and immutable architecture, which makes transactions transparent, making it a feasible choice for authentication and security purposes. Federated learning is a collaborative model training method that can benefit from Blockchain Technology. It is vulnerable to a single point of failure while performing aggregation in a centralized server architecture. Blockchain-aided Federated Learning can be proposed for enhancing the security and privacy of user data together with removing a single aggregator. It also supports a mechanism of giving rewards to users in the form of cryptocurrency to encourage users to follow the protocol. This paper introduces a framework named Blockchain-Aided privacy preserving framework for Federated Learning (BPPF) that helps in model training on the user side with added security and privacy.
Epilepsy is a long-term neurological problem that makes it hard for people to function their daily lives normally because their seizures can happen at any time. People who have epileptic seizures often hurt their brai...
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ISBN:
(数字)9798350373783
ISBN:
(纸本)9798350373790
Epilepsy is a long-term neurological problem that makes it hard for people to function their daily lives normally because their seizures can happen at any time. People who have epileptic seizures often hurt their brains, which can lead to memory loss, mental decline, and other problems. Because of this, it is important to find epileptic seizures quickly and get medical help for them. This paper presents the optimized two-layer long short-term memory model (2L-LSTM), and it can automatically expand retrospectively and for each patient. The program automatically creates distinctive characteristics that help identify seizure patients as either epileptic or not epileptic in real time. The proposed two layer Long Short-Term Memory (2L-LSTM) classifier classifies Epileptic or non-Epileptic signals with up to $\mathbf{9 8 \%}$ accuracy. It also gives the sensitivity, specificity and F1_score of $\mathbf{9 5 \%}$, $\mathbf{9 4 \%}$ and $\mathbf{9 6 \%}$ respectively. The Simulation results shows that proposed 2L-LSTM model perform far better as compared to 1L-LSTM.
作者：
Kiran, M.P.Deepak, N.R.
Dept of Computer Science and Engineering Kushalnagar Karnataka India
Dept. of Computer Science and Engineering Bangalore Karnataka India
Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. It is generally known that, the soil, climate, ...
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In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips in...
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The minimum completion (fill-in) problem is defined as follows: Given a graph family F (more generally, a property Π) and a graph G, the completion problem asks for the minimum number of non-edges needed to be added ...
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The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexib...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS.
The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant secur...
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This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-...
This research aims to solve many problems people face when moving to a new place, such as housing, food, transportation, language differences and optimization. By creating recommendations, it is aimed to provide self-support to facilitate the transition process. The system will consider people's interests and consider different languages and cultures and budgets of people. This is useful in war or disaster environment.
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