The use of artificial intelligence (AI) to farming techniques has become a revolutionary force in the era of precision agriculture, altering conventional practices and increasing agricultural production. With an emphasis on important areas including agricultural yield optimization, resource management, and economic sustainability, this study aims to investigate the concrete effects of AI on smart farming. The research explores real-time decision-making processes using sophisticated data analytics and machine learning algorithms, It shows how an AI-driven insights enable farmers to make wise decisions, reduce resource wastage, and improve overall farm productivity. The results show how AI may be used to promote environmentally friendly farming methods in addition to demonstrating the technology’s demonstrable benefits in terms of higher crop yields and lower production costs. Ethical questions are central to our study as we traverse the complicated junction of technology and agriculture. For data protection, fair access, and the appropriate use of new technology, ethical aspects of AI-integrated farming methods must be carefully examined. This research highlights possible obstacles and suggests risk-reduction tactics while critically examining the ethical ramifications of AI’s broad use in agriculture. In order to support the appropriate application of AI technology in smart farming, the research emphasizes the significance of developing an ethical framework that is consistent with society values.
Reconfigurable systems often require secret keys for encrypting and decrypting data. To address this need, applications requiring high security typically use physical unclonable functions (PUFs) - circuits that leverage random manufacturing variations to generate unique secret keys for each device. However, implementing PUFs on field-programmable gate arrays (FPGAs) can be challenging due to limited control over the layout. An alternative approach is the use of chaos-based random number generators, which are widely employed in chaos-based encryption and security applications. These generators provide a fast, deterministic source of randomness for operations like permutation and substitution. This paper presents an FPGA-based Chaotic Key Generator (C-KG) that is based on the Lorenz, Rossler, Chen, and Chua chaotic systems. Each system was implemented and tested for Pseudo Random Number Generation. Moreover, the proposed C-KG was implemented using VHDL and synthesized on a Zynq ZedBoard FPGA. The generator’s randomness, keyspace, and operations per bit ratio for each chaotic system using the NIST 800-22 statistical test suite have been assessed to validate the proposed approach. The C-KG was found to utilize minimal resources of the FPGA’s slices, operate at frequencies up to 190.548 MHz, and successfully pass all NIST statistical tests.
cardiovascular diseases (CVD) present an important healthiness mission. The early prediction of CVDs is paramount for efficient prevention and intervention efforts. In our research, we present a novel method for identifying the most relevant clinical features by optimizing features using the Relief determinant technique. In order to forecast CVD, an additional technique based on the Support Vector Machine (SVM) is used to the chosen relevant features. This proposed approach effectively determines the state health of a patient: healthy or affected by heart-related conditions. The results emphasize that the proposed hybrid method (Relief-SVM) significantly achieve the highest evaluation metrics; Accuracy ‘Ac=99.86%%’, Precision ‘Pr=97.36%’, Sensitivity ‘SE=98.52%’ and Specificity SP=95.84’. Compared to previous works, this procedure exhibits substantial promise in ensuring precise predictions regarding heart diseases.
The Internet of Things has revolutionized daily life, but its widespread adoption raises significant security concerns due to the sensitive data involved. To address this, we propose utilizing the lightweight LED block cipher for IoT security. In this work, we proposed the use of the LED block cipher for encrypting and decrypting data on resource-constrained IoT devices. The hardware architecture of the LED algorithm was optimized to fit the limited resources of these applications. The initial 8 -bit sequential architecture contained 210 Slices and achieved a throughput of 5.71 Mbps. To improve the performance, we introduced an 8 -bit architecture for the MixColumns sub-function, resulting in a throughput of 10.26 Mbps with 222 Slices. Additionally, we presented a 16-bit sequential architecture with 243 Slices and a throughput of 8.36 Mbps. By further optimizing the MixColumns sub-function to 8 bits, we attained a throughput of 12.58 Mbps with 240 Slices. This optimization positively impacted the area implementation and power consumption of IoT applications, making our LED hardware architecture suitable for lightweight applications that prioritize high secrecy requirements.
The article provides a comprehensive review and comparative analysis of various patch antennas utilized in Vehicle-to-Vehicle (V2V) communication scenarios. These antennas are specifically engineered to achieve high gain and desired directivity within the frequency range of 5.8 GHz to 6 GHz. Known for their ease of design and cost-effectiveness, patch antennas play a vital role in V2Vcommunication systems. The analysis is conducted using ANSYS HFSS software to evaluate different antenna parameters. Additionally, the article discusses various antenna shapes tailored for V2V communication applications. A compact antenna that operates between 2.8 GHz and 9.6 GHz for V 2 V communication systems is studied and presented. The obtained simulation results show a gain of 3 dBi and a bandwidth in the Ultra-Wideband (UWB) range from 2.8 to 9.6 GHz.
