Proposed System Research Articles

AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.

Development of a method for refining recommendations using temporal knowledge in individual insurance tasks

The subject of the research is the process of constructing recommendations in individual insurance tasks. The aim is to develop an approach for building recommendations in personalized insurance projects using temporal knowledge to adapt proposed insurance plans according to client behavior on insurance company websites. Objectives: to structure temporal rules considering the peculiarities of the online insurance recommendation process; to develop a method for constructing recommendations for insurance product selection using temporal knowledge. Conclusions: temporal rules have been structured for the task of building recommendations in personalized insurance projects. These rules generalize behavior in relative time for multiple users, enabling the generalization of user action sequences. A method for constructing online recommendations using temporal knowledge has been developed. The method consists of two phases: temporal knowledge formation and recommendation construction. The first phase is performed offline and is designed to form a base of temporal rules that generalize knowledge about user behavior. The second phase is executed online and involves refining and further utilizing temporal rules to adapt recommendations according to current user behavior. The method enables prompt adaptation of recommendations considering the user's current actions, creating conditions for increasing user trust in the recommender system's proposals.

Model of the “Economic Cross” of the Digital Platform: Opportunities and Conditions for Interindustry Technological Convergence

The platform economy is an objective stage in the evolution of the digital economy, whose development is carried out under the influence of the trend of involving a large number of SMEs and individuals in economic relations as their direct participants. Unlike the pre-digital era, when operational commercial direct interaction between small participants in economic relations turned out to be impossible, communication technologies of the second decade of the 21st century have created favorable opportunities for the development of independent businesses of individuals and small professional teams. The process of commercialization of household resources is actively taking place in the form of the development of private blogs as platforms for commercial advertising and mutual trade of goods and services by individuals online, which creates a direct competitive threat to traditional trading companies. The current stage in the development of online participation of SMEs and individuals in commercial relations is the development of outsourcing relationships, within which part of the processes implemented by large businesses independently using hired personnel and their own infrastructure will be delegated to independent network structures on a competitive basis. At the same time, in proportion to the growing popularity of e-commerce, public demands for the security of such transactions are increasing, which is especially important in conditions of sanctions uncertainty and high risks of supply chain collapse. This circumstance determines the business request for systematization of the digital space and its centralization, the terminal stage of which is an inter-industry digital platform. The purpose of the scientific article is to develop a system of proposals for the formation of a national intersectoral outsourcing platform based on the “economic cross” model. A system of innovative solutions has been developed regarding the organization of economic interaction between subjects, the procedure for financing the activities of the platform by its participants, and the distribution of indirect industry income arising from the functioning of the platform.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Automated Disaster Monitoring From Social Media Posts Using AI-Based Location Intelligence and Sentiment Analysis

Worldwide disasters like bushfires, earthquakes, floods, cyclones, and heatwaves have affected the lives of social media users in an unprecedented manner. They are constantly posting their level of negativity over the disaster situations at their location of interest. Understanding location-oriented sentiments about disaster situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI) and natural language processing (NLP), for extraction of location-oriented public sentiments on global disaster situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to disaster in 110 languages through AI- and NLP-based sentiment analysis, named entity recognition (NER), anomaly detection, regression, and Getis Ord Gi* algorithms. We deployed and tested this algorithm on live Twitter feeds from 28 September to 6 October 2021. Tweets with 67 515 entities in 39 different languages were processed during this period. Our novel algorithm extracted 9727 location entities with greater than 70% confidence from live Twitter feed and displayed the locations of possible disasters with disaster intelligence. The rates of average precision, recall, and F₁-Score were measured to be 0.93, 0.88, and 0.90, respectively. Overall, the fully automated disaster monitoring solution demonstrated 97% accuracy. To the best of our knowledge, this study is the first to report location intelligence with NER, sentiment analysis, regression and anomaly detection on social media messages related to disasters and has covered the largest set of languages.

Autonomous Trajectory Planning for Ultrasound-Guided Real-Time Tracking of Suspicious Breast Tumor Targets

Ultrasound image guidance could display the movement of soft tissue in real time, which provides an important basis for the operation path selection of tumor for examinations and interventions such as precise localization and puncture biopsy. However, factors such as tissue deformation, patient motion, and instrument contact pose great challenges for real-time smooth localization tracking of target tissues and maintaining a suitable acoustic window. Not only does it reduce the accuracy of ultrasound examination, but also may prolong the examination time. In this paper, a real-time autonomous ultrasound robot trajectory planning framework is proposed to achieve global scanning of breast tissue and real-time local tracking of suspicious tumor targets. In addition, an ultrasound suspicious tumor target next moment motion attitude and position estimation algorithm and an acceleration-continuous online trajectory generation (ACOTG) algorithm are proposed. Experiments on breast phantom showed that the fluctuation difference of both global scanning and tracking scanning force was less than 5 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 15%, and ACOTG takes less than 1 microsecond to calculate the next moment state. The position, velocity, and acceleration of the tracking path did not change abruptly when the unknown target changed. The ultrasound image of the suspected tumor target could always be smoothly maintained in a suitable acoustic window, while the errors between the center of the suspected tumor target image and the center of the acoustic window in the horizontal direction and the depth direction were less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 1 mm and 30 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 10%, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this study was to solve the clinical problem of difficulty in keeping the suspected breast tumor target stably in the center of the field of view under the influence of tissue deformation, patient motion and instrument contact in ultrasound guidance. We proposed the idea of global conventional scanning and local online tracking to achieve the autonomous scanning, detecting and tracking of suspicious tumor targets within the global scope of the breast. ACOTG algorithm is proposed and implemented, which can make the ultrasound image of suspicious tumor target can be displayed stably in the center of the acoustic window. With the help of this system, the accuracy of ultrasound examination can be improved and the time of examination can be reduced. The proposed system can also be applied to ultrasound scanning of other parts of the body.

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

يعد تشفير النص في الفيديو أداة حيوية في العديد من الصناعات حيث تكون حماية المعلومات الحساسة هي الأكثر أهمية. عادةً ما تواجه الخوارزميات وطرق الكشف الآلي صعوبة في تحديد موقع النص المخفي في إطارات الفيديو. تزيد هذه المقاومة من أمان المعلومات المخفية لأنه من الصعب على الأطراف غير المدعوة اكتشاف وجود نص مشفر. كما أنه يجمع بين المحتوى المكتوب والمرئي لتقديم كلا النوعين من المعلومات في وقت واحد وبسلاسة. في هذا البحث ، نقدم طريقة جديدة لتشفير النص داخل الفيديو باستخدام خوارزمية التشفير Salsa20 و Harris Corner Detection. الهدف من هذه الدراسة هو زيادة سرية وأمن المعلومات النصية المضمنة في محتوى الفيديو ، وهناك خطوتان أساسيتان في المنهجية المقترحة. لتحديد الزوايا أو النقاط المهمة داخل إطارات الفيديو ، تم استخدام طريقة Harris Corner Detection لأول مرة. تعمل هذه الزوايا كنقاط ميزة قوية يمكن استخدامها لدمج النص. تساعد طريقة Harris Corner Detection في تحديد الأماكن التي قد يتم إخفاء النص بها بدقة ، وذلك دون التقليل من جودة الصورة المرئية الإجمالية للفيديو ، وثانيًا ، يتم إنشاء مفتاح التشفير باستخدام تقنية التشفير Salsa20. Salsa20 هو تشفير دفق شائع يوفر تشفيرًا موثوقًا وتشغيلًا فعالاً. يتم تحويل النص المضمن إلى نص تشفير بواسطة XOR باستخدام مفتاح التشفير قبل تضمينه في الفيديو ، مما يضمن سريته وحمايته من الوصول غير المصرح به. تكشف مقارنة الطريقة المقترحة مع طرق تشفير النص الحالية عن عدد من المزايا. تدمج تقنية Harris Corner Detection النص في إطارات الفيديو بحيث يتم دمجها بشكل أقل وضوحًا ، مما يجعل من الصعب على الخصوم تحديد وجود المعلومات المخفية. يضمن استخدام Salsa20 أيضًا المستوى العالي من الأمان والحماية للنص المشفر ، والدفاع عنه ضد الهجمات المحتملة. يتم تقييم النظام المقترح باستخدام MSE و PSNR والارتباط و NPCR و UACI والإنتروبيا ، وتوفر مقاييس التقييم هذه نتائج ممتازة.

MR-Safe Cartesian Platform for Active Cardiac Shimming: Preliminary Validation.

Implanted Cardioverter Defibrillators (ICDs) induce a large (100 parts per million) inhomogeneous magnetic field in the magnetic resonance imaging (MRI) scanner which cannot be corrected by the scanner's built-in shim coils, leading to significant image artifacts that can make portions of the heart unreadable. To compensate for the field inhomogeneity, an active shim coil capable of countering the field deviation in user-defined regions was designed that must be optimally placed at patient-specific locations. We aim to develop and evaluate an MR-safe robotic solution for automated shim coil positioning. We designed and fabricated an MR-safe Cartesian platform that holds the shim coil inside the scanner. The platform consists of three lead screw stages actuated by pneumatic motors, achieving decoupled translations of 140 mm in each direction. The platform is made of plastics and fiberglass with the control electronics placed outside the scanner room, ensuring MR safety. Mechanical modeling was derived to provide design specifications. Experiments show that the platform achieves less than 2 mm average motion error and 0.5 mm repeatability in all directions, and reduces the adjustment time from 5 min to a few seconds. Phantom and animal trials were conducted, showing that the proposed system is able to position a heavy shim coil ( kg) for improved ICD artifact suppression. This robotic platform provides an effective method for reliable shim coil positioning inside the scanner. This work contributes to improving cardiac MRI quality that could facilitate accurate diagnosis and treatment planning for patients with implanted ICDs.

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.

Development of a Motion Controller for the Electric Wheelchair of Quadriplegic Patients Using Head Movements Recognition

The robotic wheelchair is designed to allow independent movement for patients with severe musculoskeletal and neuromuscular disorders that interfere with normal wheelchair propulsion by hands. Control of the movement of the wheelchair necessities residual motion of the upper extremity which was not applicable for patients with upper extremity disability as in Quadriplegic patients. Several research efforts have been proposed to help quadriplegic patients independently use the robotic wheelchair. However, the proposed solutions had many problems with accuracy, simplicity, and cost. The current work aims to build an accurate, simple, and low-cost control system for robotic wheelchairs using head movement. The proposed system was based on the use of the MEMs Inertial measurement unit (IMU) to sense the head movement gestures and transfer the gestures into signals to control the wheelchair movements. A new lightweight algorithm for the detection of head gestures was proposed to improve the system’s accuracy. The proposed system has been applied and tested on volunteers. The results revealed that the accuracy of the proposed method in recognizing head movement was 97% on average.

Automatic Ground-Truth Image Labeling for Deep Neural Network Training and Evaluation Using Industrial Robotics and Motion Capture

Abstract The United States Navy (USN) intends to increase the amount of uncrewed aircraft in a carrier air wing. To support this increase, carrier-based uncrewed aircraft will be required to have some level of autonomy as there will be situations where a human cannot be in/on the loop. However, there is no existing and approved method to certify autonomy within Naval Aviation. In support of generating certification evidence for autonomy, the United States Naval Academy (USNA) has created a training and evaluation system (TES) to provide quantifiable metrics for feedback performance in autonomous systems. The preliminary use case for this work focuses on autonomous aerial refueling. Prior demonstrations of autonomous aerial refueling have leveraged a deep neural network (DNN) for processing visual feedback to approximate the relative position of an aerial refueling drogue. The training and evaluation system proposed in this work simulates the relative motion between the aerial refueling drogue and feedback camera system using industrial robotics. Ground-truth measurements of the pose between the camera and drogue are measured using a commercial motion capture system. Preliminary results demonstrate calibration methods providing ground-truth measurements with millimeter precision. Leveraging this calibration, the proposed system is capable of providing large-scale datasets for DNN training and evaluation against a precise ground truth.

A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)

PurposeWe introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images. ParticipantsA total of 3,456 adults aged between 51 and 102 years of whom OCT images were collected under the PINNACLE project. MethodsOur system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome MeasuresWe checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late stage wet and dry AMD against an established clinical grading system and a demographic baseline model. ResultsOverall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. ConclusionsUsing self-supervised deep learning we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

Active intrusion detection and prediction based on temporal big data analytics

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks.

On the Need to Develop a Federal Program to Optimize the Economic Base of Public Control in Russia

Annotation. This article is devoted to the analysis of the prospects for the development and adoption of a federal program aimed at optimizing the economic basis of public control in the Russian Federation. The authors substantiate the position that one of the main problems hindering the development of public control in the country is its weak economic base. Due to the fact that public authorities play an important role in the process of formation and functioning of subjects of public control in Russia, the basis for financing the organization and activities of public control are funds from state and municipal budgets. In this regard, the authors substantiate the need for the development and adoption by the Government of the Russian Federation of a comprehensive federal program dedicated to strengthening organizational, technological, material and property independence and independence of subjects of public control. The work substantiates the need to include in the draft of the above-mentioned federal program sections devoted to: ensuring the material and technical independence of subjects of public control from public authorities; ensuring their financing primarily from protected items of public budgets; development of processes for the use of modern information and communication technologies by these entities; consolidation of a system of criteria for assessing the effectiveness and efficiency of the work of subjects of public control as the main condition for increasing the volume of their funding; consolidating a system of control on the part of public authorities regarding budget financing of the activities of these entities; systems for organizing public-private partnerships in the field of public control. In addition, the work substantiates the system of proposals for participation in the development of the above-mentioned federal program of the country's Public Chamber.

RFID BASED BUS TICKETING SYSTEM

The main objective of this paper is to generate passenger tickets using RFID technology for the fare of a particular destination. The appropriate amount is deducted from the RFID card. In addition to that, in proposal system RFID has proven to be one of the most promising technologies in recent years and can be effectively employed in various applications since it is economical and widely used tool for tracking and locating purposes.Radio-frequency identification (RFID) is a technology that uses electromagnetic fields to automatically identify and track tags attached to objects .A small radio transponder, a radio receiver, and a radio transmitter make up an RFID system The RFID application, on the other hand, has become a popular tool for both tracking transit vehicles and the public ticketing system describes a design of IoT enabled real time bus tracking system There is an mobile app to recharge the RFID card.The message of money transaction is send to the users registered mobile number.The main aim of this project is to reduce the fraud in ticket transaction.The bus door will open only after scanning the RFID card.In this project we use IR sensor to detect the passengers entry and exit from the bus.When the passenger scan the RFID card then the LCD display shows the passengers information like their name,RFID card number,account balance.The ticket price is based on the distance travelled in the bus.This system also used to prevent the unfair ticket price for transportation.If the RFID card is missing then the passenger can able to block the RFID card to prevent unauthorized access.Public transportation is a significant role for methods among individuals. Key words—Radio Frequency Identification System (RFID), Infrared sensor (IR sensor).

An FPGA-Accelerated Quartz Crystal Measurement System Based on a Negative Feedback Loop With Quadrature Demodulation

Rapid measurement of the resonant frequency of quartz crystal (QC) devices plays a key role in applying QC-based dynamic monitoring, such as respiration monitoring and kinetic process in electrochemical reactions. This work develops an FPGA-accelerated QC measurement system without software-based curve fitting. A negative feedback loop based on the susceptance response collected by the quadrature demodulation circuit is constructed. Moreover, a proportional-integral-differential control and tracking strategy is applied to improve the measurement speed significantly. The test results show that the implemented system can track a frequency shift of 5 kHz bandwidth within 50 ms. The standard deviation of the measured frequency of a QC device under test is only 0.17 Hz, which indicates that the proposed system is high stability and high precision. The frequency measurement results have a relative error of 0.8 ppm compared with the Agilent 4294A impedance analyzer. It is demonstrated that the proposed QC measurement system has great potential for QC-based dynamic detection.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy.

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.