{"title":"De la ruse à la schizophrénie : la réécriture du Chat botté dans les fictions pour adultes","authors":"N. Langbour","doi":"10.58282/colloques.7700","DOIUrl":"https://doi.org/10.58282/colloques.7700","url":null,"abstract":"","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89302790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quand le chat devient singe dans les contes arabo-berbères : un rapport filial à l’animal","authors":"Bochra Charnay","doi":"10.58282/colloques.7695","DOIUrl":"https://doi.org/10.58282/colloques.7695","url":null,"abstract":"","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79399857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-21DOI: 10.33897/FUJEAS.V2I1.463
M. Shaheen
{"title":"Editorial: Research In Engineering","authors":"M. Shaheen","doi":"10.33897/FUJEAS.V2I1.463","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.463","url":null,"abstract":"","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86697295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.33897/FUJEAS.V2I1.458
Abdur Rehman
Programming over the air (POTA) is commonly used to update the firmware and configuration of a wireless sensor node without any physical contact with the node.We used this concept here to program the Arduino pro mini wirelessly over the Bluetooth link using HC-05 module.Bluetooth module only support UART traffic to communicate with slave devices. To implement POTA, a software layer is written for HC-05 module, this software layer makes HC-05 able to program Arduino pro mini over serial communication. The written software transfer data over the Bluetooth link to the slave hardware and then Arduino pro mini is programmed.
无线编程(POTA)通常用于更新无线传感器节点的固件和配置，而无需与节点进行任何物理接触。我们在这里使用这个概念通过蓝牙链路使用HC-05模块对Arduino pro mini进行无线编程。蓝牙模块只支持UART流量与从设备通信。为了实现POTA，为HC-05模块编写了软件层，该软件层使HC-05能够通过串行通信对Arduino pro mini进行编程。写入的软件通过蓝牙链路将数据传输到从机硬件，然后对Arduino pro mini进行编程。
{"title":"A Novel Software Layer to Program Arduino over the Air using Bluetooth","authors":"Abdur Rehman","doi":"10.33897/FUJEAS.V2I1.458","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.458","url":null,"abstract":"Programming over the air (POTA) is commonly used to update the firmware and configuration of a wireless sensor node without any physical contact with the node.We used this concept here to program the Arduino pro mini wirelessly over the Bluetooth link using HC-05 module.Bluetooth module only support UART traffic to communicate with slave devices. To implement POTA, a software layer is written for HC-05 module, this software layer makes HC-05 able to program Arduino pro mini over serial communication. The written software transfer data over the Bluetooth link to the slave hardware and then Arduino pro mini is programmed.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81183903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.33897/FUJEAS.V2I1.420
Taimur Shahzad
Kobe Bryant was one of the best players of Basketball. Data regarding his 20 years played games is available on the Kaggle. We transform the categorical features by PCA and normalize the data by minmax normalization technique. Machine learning techniques such as logistic regression, Random Forest, Linear Discriminant Analysis, Naïve bayes, Gradient Boosting, Adaboost and Neural Network are applied on pre-processed data to classify whether he made shot or not. The prediction accuracy of LR, RF, LDA, NB, GB, ABC and ANN is 67.84%,64.22%,67.82%,0.61%,67.8%,68% and 67% respectively on hold an out method. The experimental results shows that Adaboost has highest prediction accuracy as compared to others method with 5 cross validations. Finally, we have got satisfactory results as compared to our benchmark (Kaggle).
{"title":"Kobe Braynt Shot Prediction using Machine Learning","authors":"Taimur Shahzad","doi":"10.33897/FUJEAS.V2I1.420","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.420","url":null,"abstract":"Kobe Bryant was one of the best players of Basketball. Data regarding his 20 years played games is available on the Kaggle. We transform the categorical features by PCA and normalize the data by minmax normalization technique. Machine learning techniques such as logistic regression, Random Forest, Linear Discriminant Analysis, Naïve bayes, Gradient Boosting, Adaboost and Neural Network are applied on pre-processed data to classify whether he made shot or not. The prediction accuracy of LR, RF, LDA, NB, GB, ABC and ANN is 67.84%,64.22%,67.82%,0.61%,67.8%,68% and 67% respectively on hold an out method. The experimental results shows that Adaboost has highest prediction accuracy as compared to others method with 5 cross validations. Finally, we have got satisfactory results as compared to our benchmark (Kaggle).","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76583035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.33897/FUJEAS.V2I1.380
Atif Ali
Artificial intelligence (AI) is trending in the military and safety-critical application sectors. Currently, the private sector is helping the government sector to implement new advanced techniques to bring a revolution for different government and public sector management. It also helps to provide sustainable accountability in the accounting field; at present, AI is bringing a revolution in concept building. It is bringing potential revolutions by using novel approaches in such directions. This paper is a novel approach in the same direction; our research aim of this paper is to emphasize the AI in the militaries, what are the latest trend and usages recently worldwide used for AI applications in militaries. In this paper, we not only discuss the usage of AI applications in the military but also in the civil defense and health industry. We review and discuss that AI has potential benefits in military applications, HRMS, decision making, disaster prevention and response, GIS, service personalization, interoperability, extensive data analysis, anomaly and pattern recognition, intrusion detection, and new solution discovery using the highly configurable system and real-time simulation.
{"title":"Artificial Intelligence Potential Trends in Military","authors":"Atif Ali","doi":"10.33897/FUJEAS.V2I1.380","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.380","url":null,"abstract":"Artificial intelligence (AI) is trending in the military and safety-critical application sectors. Currently, the private sector is helping the government sector to implement new advanced techniques to bring a revolution for different government and public sector management. It also helps to provide sustainable accountability in the accounting field; at present, AI is bringing a revolution in concept building. It is bringing potential revolutions by using novel approaches in such directions. This paper is a novel approach in the same direction; our research aim of this paper is to emphasize the AI in the militaries, what are the latest trend and usages recently worldwide used for AI applications in militaries. In this paper, we not only discuss the usage of AI applications in the military but also in the civil defense and health industry. We review and discuss that AI has potential benefits in military applications, HRMS, decision making, disaster prevention and response, GIS, service personalization, interoperability, extensive data analysis, anomaly and pattern recognition, intrusion detection, and new solution discovery using the highly configurable system and real-time simulation.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89980562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.33897/FUJEAS.V2I1.424
Sajidullah S. Khan, M. Sharif, M. I. Niass, Mehtab Afzal, Muhammad Shoaib
The early diagnosis of breast tumor detection is the most significant research issue in mammography. Computer-aided diagnosis (CAD) is one of the highly essential methods to prevent breast cancer. This research work explored the effectiveness of deep-based pixel-wise segmentation models for low energy X-rays (mammographic imagery) to detect tumors in the breast region. For this purpose, various semantic segmentation models were incorporated into the experimental procedure. All the models were analyzed using the medical images dataset, which was gathered and annotated from one of the largest teaching hospitals in the Khyber Pakhtunkhwa province, known as Lady reading hospital. It is coordinated in cooperation with local health specialists, radiologists, and technologists. The comparative analysis of the incorporated segmentation techniques' performance was observed, selecting the most appropriate model for detecting tumors and normal breast regions. The experimental evaluation of the proposed models performs efficient detection of tumor and non-tumor areas in breast mammograms using traditional evaluation metrics such as mean IoU and Pixel accuracy. The performance of the semantic segmentation techniques was evaluated on two datasets (Cityscapes and mammogram). Dilation 10 (global) performed the best among the four semantic segmentation models by achieving a higher pixel accuracy of 93.69%. It reflects the effectiveness of the pixel-wise segmentation techniques by outperforming other state-of-the-art automatic image segmentation models.
{"title":"Comparison of multiple deep models on semantic segmentation for breast tumor detection","authors":"Sajidullah S. Khan, M. Sharif, M. I. Niass, Mehtab Afzal, Muhammad Shoaib","doi":"10.33897/FUJEAS.V2I1.424","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.424","url":null,"abstract":"The early diagnosis of breast tumor detection is the most significant research issue in mammography. Computer-aided diagnosis (CAD) is one of the highly essential methods to prevent breast cancer. This research work explored the effectiveness of deep-based pixel-wise segmentation models for low energy X-rays (mammographic imagery) to detect tumors in the breast region. For this purpose, various semantic segmentation models were incorporated into the experimental procedure. All the models were analyzed using the medical images dataset, which was gathered and annotated from one of the largest teaching hospitals in the Khyber Pakhtunkhwa province, known as Lady reading hospital. It is coordinated in cooperation with local health specialists, radiologists, and technologists. The comparative analysis of the incorporated segmentation techniques' performance was observed, selecting the most appropriate model for detecting tumors and normal breast regions. The experimental evaluation of the proposed models performs efficient detection of tumor and non-tumor areas in breast mammograms using traditional evaluation metrics such as mean IoU and Pixel accuracy. The performance of the semantic segmentation techniques was evaluated on two datasets (Cityscapes and mammogram). Dilation 10 (global) performed the best among the four semantic segmentation models by achieving a higher pixel accuracy of 93.69%. It reflects the effectiveness of the pixel-wise segmentation techniques by outperforming other state-of-the-art automatic image segmentation models.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91359420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.33897/FUJEAS.V2I1.451
Sheharyar Khan
Any sport has statistics and cricket is one of the sports where statistics are significantly important because, on the based on these statistics, players are ranked. These statistics include individual runs, wickets, and highest scores, etc. Based on statistics, players are selected for any tournament around the world. This research uses Principal Component Analysis by evaluating cricket facts and figures. This analysis tests the precise co-variation among different measurements relating to the batting and bowling abilities of players in the Pakistan Super League PSL T-20 (2016-2019) and IPL T-20 (2016-2019) utilizing the progressed factual system Principal Component Analysis. In the current investigation, PCA was utilized to rank the top ten best-performing batsmen and bowlers of the PSL and IPL. Principal Component Analysis is a dimension reduction technique that is used to reduce dataset dimensions into smaller variables. We can presume that batting ability rules over bowling capacity. This exploration is the first report in Pakistan that features the highlights of the PSL and IPL.
{"title":"Statistical Analysis of Cricket Leagues Using Principal Component Analysis","authors":"Sheharyar Khan","doi":"10.33897/FUJEAS.V2I1.451","DOIUrl":"https://doi.org/10.33897/FUJEAS.V2I1.451","url":null,"abstract":"Any sport has statistics and cricket is one of the sports where statistics are significantly important because, on the based on these statistics, players are ranked. These statistics include individual runs, wickets, and highest scores, etc. Based on statistics, players are selected for any tournament around the world. This research uses Principal Component Analysis by evaluating cricket facts and figures. This analysis tests the precise co-variation among different measurements relating to the batting and bowling abilities of players in the Pakistan Super League PSL T-20 (2016-2019) and IPL T-20 (2016-2019) utilizing the progressed factual system Principal Component Analysis. In the current investigation, PCA was utilized to rank the top ten best-performing batsmen and bowlers of the PSL and IPL. Principal Component Analysis is a dimension reduction technique that is used to reduce dataset dimensions into smaller variables. We can presume that batting ability rules over bowling capacity. This exploration is the first report in Pakistan that features the highlights of the PSL and IPL.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79669735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.33897/FUJEAS.V1I2.297
Naveed Akhtar
The extend of clever gadgets has accelerated touchy statistics trade on the Internet the usage of most of the time unsecured channels. Since a large use of RFID (Radio-frequency Identification) tags in the transportation and development industries from 1980 to 1990, with the multiplied use of the Internet with 2G/3G or 4G when you consider that 2000, we are witnessing a new generation of related objects. A massive wide variety of heterogeneous sensors may also accumulate and dispatch touchy facts from an endpoint to a global community on the Internet. Privacy worries in Iot stay essential problems in the research. This paper aims to understand and additionally grant continuing doe’s research topic, challenge, and Future Direction related to Iot security. A systematic mapping finds out about (SMS) is thus utilized on the way to organize the chosen Articles into the following classification: contribution type, Type of Research, Iot Security, and their approach. We take out an overall of twenty-four Articles in support of this systematic discover out about also they categorize the following described criterion. The findings of this SMS are mentioned and the researcher was once given hints on the possible route for future research.
{"title":"Security in the internet of Things: a systematic Mapping Study","authors":"Naveed Akhtar","doi":"10.33897/FUJEAS.V1I2.297","DOIUrl":"https://doi.org/10.33897/FUJEAS.V1I2.297","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000The extend of clever gadgets has accelerated touchy statistics trade on the Internet the usage of most of the time unsecured channels. Since a large use of RFID (Radio-frequency Identification) tags in the transportation and development industries from 1980 to 1990, with the multiplied use of the Internet with 2G/3G or 4G when you consider that 2000, we are witnessing a new generation of related objects. A massive wide variety of heterogeneous sensors may also accumulate and dispatch touchy facts from an endpoint to a global community on the Internet. Privacy worries in Iot stay essential problems in the research. This paper aims to understand and additionally grant continuing doe’s research topic, challenge, and Future Direction related to Iot security. A systematic mapping finds out about (SMS) is thus utilized on the way to organize the chosen Articles into the following classification: contribution type, Type of Research, Iot Security, and their approach. We take out an overall of twenty-four Articles in support of this systematic discover out about also they categorize the following described criterion. The findings of this SMS are mentioned and the researcher was once given hints on the possible route for future research. \u0000 \u0000 \u0000 \u0000","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87816768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-24DOI: 10.33897/FUJEAS.V1I2.321
Muhammad Zafar Iqbal
Our proposed methodology involving MFCC computation along with support Vector machine is used to perform the task of Speech Emotion Recognition (SER) of collectively five emotions named Angry, Happy, Neutral, Pleasant Surprise and Sadness. Two databases are used for this purpose: Toronto Emotion Speech Set (TESS) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). We achieved 97% accuracy with TESS and 86% accuracy with IEMOCAP respectively.
{"title":"MFCC and Machine Learning Based Speech Emotion Recognition Over TESS and IEMOCAP Datasets","authors":"Muhammad Zafar Iqbal","doi":"10.33897/FUJEAS.V1I2.321","DOIUrl":"https://doi.org/10.33897/FUJEAS.V1I2.321","url":null,"abstract":"Our proposed methodology involving MFCC computation along with support Vector machine is used to perform the task of Speech Emotion Recognition (SER) of collectively five emotions named Angry, Happy, Neutral, Pleasant Surprise and Sadness. Two databases are used for this purpose: Toronto Emotion Speech Set (TESS) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). We achieved 97% accuracy with TESS and 86% accuracy with IEMOCAP respectively.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89341925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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