computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In this paper, we showcase the integratio...
详细信息
Advances in lightweight neural networks have revolutionized computer vision in a broad range of Internet of Things (IoT) applications, encompassing remote monitoring and process automation. However, the detection of s...
详细信息
Distance measure can measure the difference between intuitionistic fuzzy sets. According to the indeterminacy of steganography communication, the statistical features of cover and stego image are regarded as two intui...
详细信息
India may be the largest exporter of farm products, but the country’s farms are not very productive. Inadequate agricultural production means farmers bring home much less money. The producers need a performance impro...
详细信息
India may be the largest exporter of farm products, but the country’s farms are not very productive. Inadequate agricultural production means farmers bring home much less money. The producers need a performance improvement if there is a rise in their revenue. Farmers may boost output by cultivating land more effectively by first determining what will thrive there. The land’s potential production can be enhanced by growing the correct crops. As a result, crop recommendation algorithms may be quite useful for agriculturalists. Crop yields are influenced by a wide variety of external influences. The production is affected by environmental conditions like as heat, moisture, soil pH, precipitation, and the availability of nutrients like potash, ammonium, & phosphate. There is a lot of confusion among farmers regarding which crops should be produced in particular regions to get the highest possible yield and financial return. As a result, the purpose of this study is to show how a ml algo may be used to forecast crops and associated prices. Soil is fundamental to the proper functioning of the terrestrial ecosystem, and its primary duty is the cultivation of crops for the purpose of providing food for the world’s ever-increasing population. Yet global soil deterioration poses a serious challenge to food security. The stresses of urbanization and industry, among others, contribute to soil degradation. Habitat loss, altered green spaces, soil erosion, unchecked livestock, illegal dumping, and ineffective land management are all major contributors to soil deterioration. More than 1.5 billion people are in danger of losing their way of life due to the rapid pace of land degradation, which now stands at 24 percent (350M ***). In this situation, keeping up with the rising demand for food is a problem made more difficult by the need to maintain soil quality). As a result, maintaining agricultural output and adapting to the uncertainties brought by climate change needs constant
The brainchild of the proposed work lies in automatic detection of autism using image segmentation method. CNN is the most powerful technique for biomedical image segmentation where several variants are proposed. The ...
详细信息
Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers’ incomes and global food s...
详细信息
ISBN:
(数字)9798331504403
ISBN:
(纸本)9798331504410
Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers’ incomes and global food supply. While current detection systems face accuracy and robustness challenges, deep learning models, particularly the EfficientNetB0, show promise. Despite their sensitivity to noise and variations in leaf appearance, ongoing research addresses these issues. This study introduces the use of EfficientNetB0 for accurate classification, incorporating a fine-tuning strategy in a brief five-epoch training period. This approach optimally balances model adaptation and prevents overfitting, leveraging the deep neural network’s ability to capture complex hierarchical features. The limited fine-tuning duration addresses potential risks while maximizing the model’s robust features. This unique amalgamation of advanced architecture and tailored training strategy aims to achieve efficient and accurate disease classification. The research signifies a step toward revolutionizing rice disease detection, emphasizing the importance of striking a balance between cutting-edge methodologies and practical considerations in the pursuit of agricultural sustainability. Before Fine Tuning the Training Accuracy was $\mathbf{9 8. 4 6 \%}$, Validation Accuracy was $\mathbf{9 9. 1 5 \%}$ and Test Accuracy was $\mathbf{9 8. 4 4 \%}$.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be $\mathbf{9 9. 7 7 \%}$, Validation Accuracy is $\mathbf{9 9. 4 3 \%}$ and Test Accuracy: 99.12%
Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving su...
Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of training despite their advantages. Furthermore, the process of performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of 95%. In addition, the response time to the user’s breathing was around 0.23 s.
Proof of Work (PoW) is the most widely adopted consensus mechanism on public blockchains. The PoW blockchain network achieves consensus by solving computational problems. If a network participant owns more than 50% of...
Proof of Work (PoW) is the most widely adopted consensus mechanism on public blockchains. The PoW blockchain network achieves consensus by solving computational problems. If a network participant owns more than 50% of the total computational power in the network, they can forge the blockchain. Hence, initial PoW blockchain networks with low computing power are vulnerable to block forgery attacks. We propose a smart contract-based checkpoint method to improve security vulnerabilities in the initial blockchain networks. In our method, participants periodically record block headers in an Ethereum smart contract. The recorded checkpoint block header validates the blockchain. Participants reject blocks with blocks that differ from the recorded checkpoints. Our method ensures the integrity of blocks until the height of the most recently created checkpoint, reducing the risk of double-spending. We optimize checkpoint costs by overlapping multiple checkpoint processes in a single transaction. The interval of our checkpoint method grows with the growth of the network, making the network less dependent on checkpoints. We analyze the performance of checkpoints in mitigating attacks and demonstrate that they significantly decrease the success probability of attacks in the network.
Nowadays, people are becoming more and more concerned with their physical health, but mental health is not given the same level of attention. Even if they are aware that they have been afflicted by chronic mental illn...
Nowadays, people are becoming more and more concerned with their physical health, but mental health is not given the same level of attention. Even if they are aware that they have been afflicted by chronic mental illnesses, many people choose not to seek treatment out of fear of what others would think, a belief that they have lost their minds, a dislike of doctors, or all three. These circumstances make it urgently necessary to find a solution so that more individuals are not inclined to mental diseases. This paper focuses on forecasting mental health using deep learning approaches and machine learning algorithm that is support vector machine. Support vector machine is used to solve the existing problem, as many machine learning and deep learning techniques are helping to solve these contemporary difficulties. SVM gives more accuracy compared to other machine learning algorithms to predict the mental illness.
Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficien...
Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time.
暂无评论