This research investigates integrating profound learning strategies into therapeutic picture investigation and illness determination, pointing to up-grade demonstrative exactness and treatment arranging. Leveraging convolutional neural networks (CNNs), repetitive neural systems (RNNs), U-Net, and ResNet designs, we conducted broad tests over different restorative imaging modalities, counting MRI, CT, X-ray, ultrasound, and histopathology pictures. Our results illustrate the predominant execution of CNNs in picture classification errands, accomplishing correctnesses of up to 95

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is driving significant advancements in smart collaboration across industries. By combining IoT's data-gathering capabilities with AI's analytical power, AIoT systems enable real-time decision-making, automation, and optimization in areas like manufacturing, healthcare, and smart cities. This chapter examines how AI interprets IoT-generated data to enhance efficiency, sustainability, and productivity. Key use cases are highlighted, demonstrating the synergy between AI and IoT in improving processes, reducing energy consumption, and bolstering safety measures. Challenges such as data security, latency, and device compatibility are addressed, with potential solutions like edge computing and secure frameworks proposed. The chapter concludes with a forward-looking perspective on how AIoT can transform industries and reshape human-machine collaboration, paving the way for smarter, more connected environments.

This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN -based approaches in leveraging raw MRI images for accurate and early detection of AD.

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference.

The field of exploring social models of smart home appliances aims to understand how these technologies interact with mobility and security issues, highlighting their broader social implications. This research has emerged due to the ubiquity of smart home devices and the need to comprehend their social impact. It focuses on the interplay between technology and its effects on equity and privacy concerns, aiming to develop policies that prioritize users over technology providers, while ensuring the protection of both smart home technologies and user privacy. The main challenge is understanding the integrated social safety functionality of these appliances. By examining the relationships between different countries, the research seeks to address differences through an approach that includes cultural norms, technological infrastructure, legal expertise, and user education. This anticipates a shift in smart home research towards a holistic approach, considering complex interactions. The goal is to provide insights for developing flexible and socially responsive smart home technologies.

In underdeveloped and developing countries, irrigation is the most difficult aspect of agriculture. The main reason for this is that there isn't enough rain, which means that more land isn't watered. Another significant concern is the waste of water due to the indiscriminate usage of water resources. The drip system is the only way to provide water to the plant zone, which saves a lot of water. At regular intervals of power supply, an irrigation system that is automatic can deliver water to plants whenever they require it. The valves do not need to be turned on or off in this situation. This self-contained irrigation system will water plants at precise times based on soil conditions, enhancing crop growth by collecting water and nutrients as needed. The purpose of this work is to create a sensor network based on low-cost soil moisture and temperature monitoring system that can monitor soil moisture and temperature in real-time and deliver water to plants based on the values detected and crop type. Plants and trees in smart cities must be irrigated on a regular basis to maintain lush foliage, such as irrigation of city park fields and roadside plants. Earlier methods included water channeling or manual watering, both of which resulted in plant death if proper care was not taken. In addition, mechanically running water pumps for filling tanks and controlling sprinklers resulted in a waste of water and electricity due to unnecessary activities. This IoT-based module, similar to an agriculture system or a drip system, provides for optimal plant development while yet conserving the natural beauty of the area. With its efficiency, the ground-level deployment achieves the goal. The proposed model saves approximately 51% water against the conventional watering model.

In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent research endeavors will prioritize the validation of our model in more extensive, multi-center cohorts and the examination of its efficacy in forecasting the advancement of detection and the effectiveness of therapeutic interventions.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder, making early detection of symptoms vital for timely intervention and improved patient outcomes. This chapter investigates the potential of social media data combined with natural language processing (NLP) techniques to identify early signs of PD and promote public awareness. We propose a comprehensive framework encompassing data collection, preprocessing, analysis, and machine learning models for identifying PD symptoms. Our findings demonstrate the effectiveness of this approach in detecting early signs of PD and fostering public awareness through the analysis of social media posts. The implications of this research extend to the development of innovative methods for monitoring and managing various health issues in real-time using social media data.

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent research endeavors will prioritize the validation of our model in more extensive, multi-center cohorts and the examination of its efficacy in forecasting the advancement of detection and the effectiveness of therapeutic interventions.

One of the most important problems towards studying complicated networks is community detection. The methodology of non-negative matrix factorization (NMF) has lately emerged as among the hottest research issues within community detection because of its ability to reveal natural structures and trends in high-dimensional information. The primary difficulty is that most community detection methods are hampered by the issue of nodes belonging to several communities. We will use the NMF technique in this work to tackle this issue by creating a novel mathematical function. In addition, we will include a regularized factor for simulating latent embedding space and a correlation factor to prevent overlap inside nodes that belong to various communities. Following that, the entire objective function will employ an optimization approach to arrive at the variable values that are ideal. Finally, we assess the effectiveness of different methodologies on real networks. According to experimental findings, the introduced approach is better among the other state-of-the-art methodology.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Individualized head related transfer functions (HRTFs) were used to process brief noise bursts for a two-interval forced choice front/back shift between virtual sound source locations presented via two models of headphones, the frequency responses of which could be made nearly flat for each of 21 listeners using their own individualized headphone equalization filters. In order to remove tone coloration differences between virtual sources processed using individualized HRTFs measured in front or in back of each listener, the spectral centroid of the “back” source was adjusted to more nearly match that of the source processed using the “front” HRTF. This manipulation resulted in chance levels of discrimination performance for 12 out of 21 listeners. For the remaining nine listeners showing good discrimination, the virtual sources presented using individualized headphone equalization supported significantly better front/back discrimination rates than did virtual sources presented without correction to headphone responses.

In the most demanding virtual auditory display applications, in which individualised Head Related Transfer Functions (HRTFs) are used for the presentation of virtual sound sources via headphones, there is controversy regarding how important it may be for individualised Headphone Transfer Function (HpTF) measurements to be used in equalizing the headphone response for each listener. In order to test what impact the use of such individualized HpTFbased correction might have on directional judgments, filtered noise bursts were presented with and without such headphone correction during a test of front/back hemifield discrimination for virtual sound sources positioned on six sagittal planes offset from the median plane by 15 o , 30 o , and 45 o to either side. While perfect discrimination performance was observed given repeated two-interval forced choice discrimination trials in which a pair of short noise bursts were presented using individualised HRTFs, within-trial variation in the spectrum of the source submitted to HRTF-based processing made the task quite difficult, reducing performance to chance levels for 7 of the 17 listeners tested. For the remaining listeners who showed above-chance performance under all conditions tested, performance levels were well below the perfect performance that had been observed when the spectrum of the HRTF-processed source was held constant. Through inter-stimulus variation in source spectra, which functioned to remove the socalled “known-source-spectrum ceiling effect” associated with simple laboratory tests of virtual auditory display technology, it was possible to show that front/back discrimination performance was clearly affected when sources were processed using headphone correction filters that were based upon a each individual’s measured HpTF.